License

There are not guarantees or assurances provided, all examples are selected for demonstration purposes.

MIT

Copyright (c) 2020-2021 by Gustavo Zambrana

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Setup

Python Libraries

import os
print( os.environ['PYTHONPATH'] )
%reload_ext autoreload
%autoreload 2


# Python ≥3.6 is required
import sys
assert sys.version_info >= (3, 6)

# Common imports
import numpy as np
import os
import pandas as pd
import yfinance as yf
import feather
import tables
import ta
from datetime import datetime

# Ignore useless warnings (see SciPy issue #5998)
import warnings
warnings.filterwarnings(action="ignore", message="^internal gelsd")

import sqlalchemy
print(sqlalchemy.__version__)
from sqlalchemy.orm import with_polymorphic

from trade_engine.persist.DBLayer                             import DBLayer
from trade_engine.persist.DBSchema                            import DBSchema

/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/src:/Users/gustavozambrana/Google Drive/Python:/Users/gustavozambrana/Google Drive/Python/Projects/Market_Data/src
1.4.0b3

Logging

import logging
from logging.config import fileConfig
fileConfig('/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/log/logging.ini')

#logger = logging.getLogger('dev')
import logging
logging.basicConfig(level=logging.INFO)
logging.getLogger().setLevel(logging.INFO)

Database

myDBLayer = DBLayer()

# ***
# *** Connect String for SQL Lite db in memory
# ***
# sqliteConnString = 'sqlite:///:memory:' 
# myDBLayer.connect( connString = sqliteConnString )

# ***
# *** Connect String for SQL Lite db file
# ***

# dbTradeEngTestPath = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/db/dbTradeEngTest.db'
# ConnString = 'sqlite:///' + dbTradeEngTestPath

# ***
# *** Postgres DB
# ***

ConnString = 'postgresql+pg8000://postgres:postgres@localhost:7777/trade_engine_dev_db'

# *** Connect to DB, Create DB Schema if not already created, and acquired a DB session

myDBLayer.connect( connString = ConnString )
DBSchema.createDBSchema()  
session = DBLayer.getSession()

conn string =  postgresql+pg8000://postgres:postgres@localhost:7777/trade_engine_dev_db

Optional Drop Table(s)

DBSchema.dropAll()

Trade Engine Overview

Architecture

• The purpose of the engine is:

  • Performs a backtest for a single trade strategy or a portfolio of trade strategies

  • Support different kinds of backtests and optimizations of hyper-parameters per trade strategy

  • Analyzed out-of-sample performance for trade strategies and/or portfolio

    • Support Technical Analysis Models, as well as AI Based Models to manage all trade actions, that is:

      • Enter, Exit, Position Sizing, Stop-Loss, Trail-Stop-Loss, etc.

      • Technical Analysis Models are modelts that:

        • Do not need to be fitted prior to a backtest, such as: SMA, RSI, Bollinger Bands, MACD, etc.

      • AI Based Models are any models that:

        • Required to be fitted with a training and validation date ranges that do not overlapped the backtest period, such as Machine Learning, Deep Learning, and Reinformcement Learning Models.

  • Support ensemble models for any trade action in a Trade Strategy, i.e.:

    • N number of models for entering a trade
    • M number of models to exit a trade
    • K number of models to decide on stop-loss %

  • Support mixing Technical Analysis and AI Based Models in ensembles

  • Support persisting all information to execute a backtest in a Relational Database, as well as the results

  • Setup the initial architecture to enable distributed cloud execution of backtests (future)
    
    

• A **Trade Strategy** is defined via a Trade Asset Register Item, which is an object that contains all components necessary to execute in a backtest
  
  
• A **Trade Asset Register Item** has several components:
  • Trade Strategy Model
    • Manages the actions to perform in a trade strategy (Enter, Exit, Position Size, Stop-Lost, etc.)
    • Has a reference to a Market Data Manager Object to get the required data
  • Trade Asset Investment Manager
    • Manages the funds allocated to the trade strategy
  • Broker
    • Open, closes, and updates trades (Simulated or Paper-Trading)
      
      
• A **Trade Strategy Model** has different models controlling the different actions in a trade
  • A list of Enter Action models, and corresponding policy rules for the ensemble (required)
  • A list of Exit Action models, and corresponding policy rules for the ensemble (optional)
  • List of models for all other actions and their ensemble policies (optional)
    
    
• A **Portfolio Register Item** defines a portfolio
  • It is an object that contains a list of Trade Asset Register Items and a Portfolio Investment Manager
  • The Portfolio Investment Manager, manages the funds for the entire portfolio, and re-balances the funds for the portfolio, if configured to do so, periodically.

Backtest' Types

There are different types of backtest supported, the following is a list of the different backtests types and their objectives:

**1. Backtest**

• Executes a Trade Register Item or Portfolio Register Item in a test period
• If any of the Trade Register Items contains at least one trade-action rule that is modeled via an AI Model, the modeled Fit date ranges must be specified (Training and Validation )
• A Backtest generates a large set of performance statistics to analyze the trade strategy, and a set of trade performance plots (supported via the package: https://github.com/ranaroussi/quantstats )

**2. WFA Backtest**

• Re-fit all AI models that a given trade strategy or portfolio depends during a backtest
• The re-fit periodicity and size are configurable parameters, for ex:
  • Re-fit all AI Models every 3 months during a backtest, as the backtest progresses, it uses the updated fitted models for the action rules.

**3. Sliding Window WFA Backtest**

• Executes a set of WFA-Backtests by sliding or extending the Training, Validation and Test Periods through time, and by also varying (optionally) the re-fit frequency per backtest.
• Purpose of these set of backtest is to analyze the out-of-sample performance for trade strategies and/or portfolios through different market regimes.

**4. Grid-Search Backtest**

• Run a set of backtests by varying a list of hyper-parameters for each Trade-Strategy or set of Trade Strategies.
• Similar in spirit to https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
• The Grid-Search Backtest can be applied to a regular Backtest, a WFA-Backtest, or as an option in a Sliding-Window WFA Backtest; in the latter, the grid-search will be conducted prior to each sliding-window iteration.
• The main purpose of this type of backtest, it is optimizing hyper parameters for one or more trade strategies in a backtest or per sliding-window shifting.

**5. Production-Sim Backtest**

• The objective of this backtest is to allow the selection of a one or more trade strategies over time for trading. The trade strategies can vary over time, and have different hyper-parameters resulting from an optimization via the Grid-Search Backtest. Subsequently to selecting n number of Trade Strategies, evaluate them out-of-sample, and repeat this process many times through time using the Sliding Window WFA Backtest.
• The main idea is that the trade strategies selected for trading can vary over time, as well as the hyper-parameters of the underlying models of the trade strategies, and the re-fit frequency. One must be careful to carry this process and selection process up to certain time, and only apply once out-of-sample, otherwise, one is adding bias to the selection process.

Graphical Representation

Examples

Summary

This section gives an overview of the current examples provided, and general instructions

• The examples are categorized as follows:

  1. **Backtest**
     
     
  2. **WFA Backtest**
     
     
  3. **Sliding Window WFA Backtest**
     
     
  4. **Grid Backtest**
     
     
  5. **Production Sim Backtest**
     
     
  6. **Reports**
     
     
  7. **Other**
     
     

• There is a common set of imports for all examples at the beginning that is required to be imported
  

• The examples should be reviewed sequentially, as they get more complex and present more features as they progress. In addition, some examples refered to explanations and models from previous ones.
  

• Every example is self-contained, this leads to a bit of duplication of code. However,in the interest of maximizing clarity this approached is used.
  

• Every example has a detail description of the backtest at the start, the trade strategies in the backtest, the trade actions configured and their supporting models, their Fit Periods (only required if at least one AI model is present in the backtest), Test Period, etc.
  

• Saving and Re-Storing any objects to a database is optional, in some examples is presented, but it is the same procedure for all examples/objects. In all cases, saving an object, cascades and saves all children objects, and retreiving an object, retrieves all children objects (lazy loading). Refer to SQLAlchemy for additional details.
  

• The Trade Engine objects keep state associated with a given backtest, the only objects that should be shared between backtests are Market Data Objects, and Pipeline Data Managers, but they don't have to be.
  

Examples:

• **Backtest**

  1. Nasdaq ETF - Yahoo Finance Market Data - SMA Trade Strategy

  • **Type :** Trade Strategy
  • **Models :** SMA
  • **Market Data Source:** Yahoo Finance
  • **Description:** One trade strategy: Nasdaq using Simple Moving Average(SMA) for entering/exiting a trade

  ---

  2. Cottons Futures - Quandal Market Data - SMA Trade Strategy

  • **Type :** Trade Strategy
  • **Models :** SMA
  • **Market Data Source:** Quandal
  • **Description:** One trade strategy: Cotton's Futures using Simple Moving Average(SMA) for entering/exiting a trade

  ---

  3. Nasdaq ETF - Yahoo Finance Market Data - LSTM Trade Strategy

  • **Type :** Trade Strategy
  • **Models :** LSTM
  • **Market Data Source:** Yahoo Finance
  • **Description:** One trade strategy: Nasdaq ETF using a LSTM-Model for entering/exiting a trade

  ---

  4. Portfolio A: Long and SMA Trade Strategies for US Indixes

  • **Type :** Portfolio
  • **Models :** SMA
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using SMA

  ---

  5. Portfolio B: Long and AI LSTM based Strategies for US Indixes

  • **Type :** Portfolio
  • **Models :** LSTM
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using LSTM AI Model

  ---

  6. Portfolio C: Long and Multiple Configured SMA Trade Strategies for US Indixes

  • **Type :** Portfolio
  • **Models :** Seven distinct SMA Models
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Twenty-One trade strategies: Nasdaq, S&P 500, and DOW Jones using different SMA Models
    • 1. Three ensemble trade strategies based on all SMA models, one per US Index

  ---

  7. Portfolio D: Long and Multiple Configured LSTM Trade Strategies for US Indixes

  • **Type :** Portfolio
  • **Models :** Seven distinct LSTM Models
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Twenty-One trade strategies: Nasdaq, S&P 500, and DOW Jones using different LSTM Models
    • 1. Three ensemble trade strategies based on all LSTM models, one per US Index

  ---

  8. Portfolio E: Long and Eleven Distinct Technical Analysis Based Trade Strategies for US Indixes

  • **Type :** Portfolio
  • **Models :** Elevent distinct Technical-Analysis (TA) Models
    • SMA, Bollinger Bands, MACD, RSI, Stochastic-RSI, ADX, KAMA, TSI, ForceIndex, MFI, SMA
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Thirty-Three trade strategies: Nasdaq, S&P 500, and DOW Jones for each TA Model
    • 1. Three ensemble trade strategies based on all TA models, one per US Index

  ---

  9. Portfolio F: Long and Multiple Configured AI Modeled Trade Strategies for US Indixes

  • **Type :** Portfolio
  • **Models :** Eight distinct AI based models
    • GRU, LSTM, Conv1d-GRU, Conv1d-LSTM, Wavenet, Wavenet-GRU, GRU, Wavenet-LSTM
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Twenty-Four trade strategies: Nasdaq, S&P 500, and DOW Jones for each AI trading model
    • 1. Three ensemble trade strategies based on all AI trading models, one per US Index

  ---

  10. Portfolio G: All Trade Strategies in Portfolio E and F

  • **Type :** Portfolio
  • **Models :** Eight distinct AI based models and Eleven TA based models
    • GRU, LSTM, Conv1d-GRU, Conv1d-LSTM, Wavenet, Wavenet-GRU, GRU, Wavenet-LSTM
    • SMA, Bollinger Bands, MACD, RSI, Stochastic-RSI, ADX, KAMA, TSI, ForceIndex, MFI, SMA
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Fifty-Seven trade strategies: Nasdaq, S&P 500, and DOW Jones for each AI and TA trading model
    • 1. Three ensemble trade strategies based on all AI and TA trading models, one per US Index

  ---

  11. Portfolio H: Utilized Leveraged ETFs for Trading on The Model Trade-Asset

  • **Type :** Portfolio
    • This example shows the effect of creating a Trade Strategy with a given model for any trade asset, but actually, trading on an underlying leveraged ETF for that security
  • **Models :** Five distinct AI and TA based models
    • GRU, LSTM, Bollinger Bands, MACD, RSI
  • **Market Data Source:** Yahoo Finance
  • **Description:** Portfolio with the following trade strategies:
    • 1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold strategy
    • 1. Five Trade Strategies are executed with ETF-on
    • 1. Five Trade Strategies are executed with ETF-off to show effect of ETF-on trading
    • 1. Three ensemble trade strategies based on all AI and TA trading models, one per US Index, for the ETF-on Trade Strategies
    • 1. Three ensemble trade strategies based on all AI and TA trading models, one per US Index, for the non-ETF Trade Strategies

  ---

  12. Portfolio I: Monthly Rebalance of Funds in a Portfolio

  • **Type :** Portfolio
  • **Models :** Same strategies as defined and explained in Portfolio C
  • **Market Data Source:** Yahoo Finance
  • **Description:**
    • Portfolio Investment Manager configured to balance funds monthly, using an equal weights policy

  ---

  13. Portfolio J: Trade Strategies with different Market Data Sources (Quandal, YahooFinance, User-Defined DataFrame)

  • **Type :** Portfolio
  • **Models :**
    • Multiple SMA Models, and LSTM, GRU, MACD, and RSI Models defined to use with each trading asset
  • **Market Data Source:** Yahoo Finance, Quandal, User-Defined DataFrame
  • **Description:**
    • This example shows how to configured a portfolio with trade strategies that depend on different market data sources.
    • Each trade strategy is associated with one Market Data Source, which may or may not be shared with other trade strategies.
    • This example also shows how to use any market data with the engine, provided it is in a Pandas DataFrame (details discussed in the example, regarding the format of the data frame)

  ---

  ---

• **WFA Backtest**

  1. WFA-Backtest-1

  • **Type :** Portfolio
  • **Models :** Subset of the trade strategies in Portfolio D
  • **Market Data Source:** Yahoo Finance
  • **Description:**
    • Re-fit AI models every three months during the backtest
    • Don't anchored the training-set

  ---

  2. WFA-Backtest-2

  • **Type :** Portfolio
  • **Models :** Subset of the trade strategies in Portfolio G
  • **Market Data Source:** Yahoo Finance
  • **Description:**
    • Re-fit AI models every six months during the backtest
    • Anchored the training-set

  ---

  ---

• **Slide Window WFA Backtest**

  1. SW-WFA-Backtest 1

  • **Type :** Portfolio
  • **Models :** Subset of the trade strategies in Portfolio D
  • **Market Data Source:** Yahoo Finance
  • **Description:**
    • Re-fit AI models every three months, then every six months within a backtest
    • Fixed Training and Validation periods
    • Training-set is not anchored during a backtest

  ---

  2. SW-WFA-Backtest 2

  • **Type :** Portfolio
  • **Models :** Subset of the trade strategies in Portfolio D
  • **Market Data Source:** Yahoo Finance
  • **Description:**
    • Re-fit AI models every six months within a backtest
    • Slide Datasets by one year increments for five consecutive years
    • Training-set is not anchored in outer slide window or during a backtest

  ---

  3. SW-WFA-Backtest 3

  • **Type :** Portfolio
  • **Models :** Subset of the trade strategies in Portfolio G
  • **Market Data Source:** Yahoo Finance
  • **Description:**
    • Re-fit AI trading models every 6 months within a backtest and anchored within the backtest
    • Fit datasets are anchored and duration increased by one year increments for five consecutive years
      • Every sliding window iteration, the training set duration is increased by 9 months and the validation set by 3 months, the test set is just shifted by one year.
    • Training-set is anchored in outer slide window and also anchored in the re-fitting process during a backtest

  ---

  ---

• **Grid Backtest**

  To be added

  ---

  ---

• **Production Sim Backtest**

  To be added

  ---

  ---

• **Other**

  To be added

Backtest

# *** Imports required for running Trade Strategy examples

from trade_engine.model.TradeStrategyBaseModelPersist                import TradeStrategyBaseModelPersist
from trade_engine.model.TradeStrategyMarketDataMng                   import TradeStrategyMarketDataMng
from trade_engine.model.MarketDataPeriod                             import MarketDataPeriod
from trade_engine.ts_actions.generate.TradeStrategyActionGenerator   import TradeStrategyActionGenerator
from trade_engine.ts_actions.generate.TradeStrategyConfigData        import TradeStrategyConfigData
from trade_engine.investment_manager.TradingAssetInvMngBase          import TradingAssetInvMngBase
from trade_engine.registry.TradingAssetExecutionType                 import TradingAssetExecutionType
from trade_engine.registry.TradingAssetRegisterItem                  import TradingAssetRegisterItem
from trade_engine.broker.SimBrokerTrading                            import SimBrokerTrading
from trade_engine.execution.BacktestExecution                        import BacktestExecution
from trade_engine.dataset_def.DatasetDef                             import DatasetDef, DatasetType
from trade_engine.dataset_def.FitDatasetsDef                         import FitDatasetsDef
from trade_engine.reports.BacktestReports                            import BacktestReports

# *** Additional Imports required for running Portfolio examples

from tradeg.market_data.MarketData                                   import MarketData
from trade_engine.model.TALibPipelineBuilder                         import TALibPipelineBuilder
from trade_engine.model.TradeStrategyModelPipelineDataMng            import TradeStrategyModelPipelineDataMng
from trade_engine.enter_ts.TradeEnterClassicStrategy                 import TradeEnterClassicStrategy
from trade_engine.exit_ts.TradeExitClassicStrategy                   import TradeExitClassicStrategy
from trade_engine.model.classic.CacheTradingModel                    import CacheTradingModel
from trade_engine.model.classic.TradingRulesModel                    import TradingRulesModel
from trade_engine.model.classic.VotingTradingRulesModel              import VotingTradingRulesModel
from trade_engine.exit_ts.TradeExitStack                             import TradeExitStack
from trade_engine.enter_ts.TradeEnterStack                           import TradeEnterStack
from trade_engine.investment_manager.PortfolioInvMng                 import PortfolioInvMng
from trade_engine.investment_manager.PortfolioRebalanceFrequency     import PortfolioRebalanceFrequency
from trade_engine.registry.PortfolioRegisterItem                     import PortfolioRegisterItem
from trade_engine.model.ai.LSTM_Model                                import LSTM_Model
from trade_engine.model.ai.WaveNet_RNN_AI_TradingModel               import WaveNet_RNN_AI_TradingModel
from trade_engine.model.ai.WaveNet_RNN_AI_TradingModel               import WaveNet_RNN_AI_Trading_Rules
from trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder            import WaveNet_RNN_AI_PipelineBuilder
from trade_engine.enter_ts.TradeEnterAIStrategy                      import TradeEnterAIStrategy
from trade_engine.exit_ts.TradeExitAIStrategy                        import TradeExitAIStrategy
from trade_engine.ts_actions.policy.TradeEnterSetPolicy  import TradeEnterSetPolicy, TradeEnterSetVotingPolicy
from trade_engine.ts_actions.policy.TradeEnterPolicy     import TradeEnterPolicy
from trade_engine.ts_actions.policy.TradeExitSetPolicy   import TradeExitSetPolicy, TradeExitSetVotingPolicy
from trade_engine.ts_actions.policy.TradeExitPolicy      import TradeExitPolicy

# *** Additional Imports required for running WFA Backtest and Slide Win WFA Backtest

from trade_engine.execution.BacktestExecutionWFA             import BacktestExecutionWFA, WFASlideWindowPeriod
from trade_engine.execution.BacktestExecutionSlideWindowWFA  import BacktestExecutionSlideWindowWFA
from trade_engine.execution.BacktestExecutionSlideWindowWFA  import WFASlideWinScenarioFixDatasets
from trade_engine.execution.BacktestExecutionSlideWindowWFA  import WFASlideWinScenarioSlidingDatasets
from trade_engine.execution.BacktestExecutionSlideWindowWFA  import WFASlideWinScenarioAnchoredSlidingDatasets

Trade Strategies

Nasdaq ETF - Yahoo Finance Market Data - SMA Trade Strategy

• SMA Trade Strategy for Nasdaq ETF

  Description:

    1. $1.0M Budget
    2. 100% capital invested
  
  

  Trade Strategies:

    1. One trade strategy: Nasdaq using Simple Moving Average(SMA) for entering/exiting a trade
  
  

  Backtest:

    1. Type: regular
    2. Test Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2005-12-31 

Trade Asset

trade_assets   = [ "^IXIC" ]

Market Data Manager

Market Data Manager configured to use yahoo-finance for obtaining market data

The following TradeStrategyMarketDataMng object will do the following:

1. Download market data for: 
    [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
2. Used yahoo-finance as the market provider
3. Save the downloaded data in the directory and file specified by the arguments:
   1. dirPathMarketData
   2. fileNameMarketData
4. It will request the market data provider to get data from "beg_date_str" to "end_date_str"
5. If end_date_str is not defined (as the case below), all data available from the begin date is 
   requested.
6. Interval is set to "1d", this parameter is market data provider specific, see Yahoo-Finance, 
   Quandal for what options they support
7. The **Adj Close** column is configured as the default price column 
8. Note that even so leveraged ETFs are requested from 2000-01-01 to the present, the data 
   actually returned for these stock symbols by Yahoo-Finance doesn't start until 2011.
   The behavior is market data provider specific

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-11-02 09:24:16,038 [INFO] root: Found previous Market data previously loaded to specified file and dir.

ts_marketdat_mng.marketDataObj.getDataFrame()

	Index	Adj Close	Close	High	Low	Open	Volume
Date
2000-01-03	^DJI	11357.509766	11357.509766	11522.009766	11305.690430	11501.849609	169750000
2000-01-03	^GSPC	1455.219971	1455.219971	1478.000000	1438.359985	1469.250000	931800000
2000-01-03	^IXIC	4131.149902	4131.149902	4192.189941	3989.709961	4186.189941	1510070000
2000-01-04	^DJI	10997.929688	10997.929688	11350.059570	10986.450195	11349.750000	178420000
2000-01-04	^GSPC	1399.420044	1399.420044	1455.219971	1397.430054	1455.219971	1009000000
...	...	...	...	...	...	...	...
2021-09-20	TQQQ	133.490005	133.490005	137.460007	128.000000	135.419998	60721113
2021-09-20	UDOW	69.760002	69.760002	71.459999	67.519997	70.220001	6439708
2021-09-20	^DJI	33970.468750	33970.468750	34459.718750	33613.031250	34459.718750	441645657
2021-09-20	^GSPC	4357.729980	4357.729980	4402.950195	4305.910156	4402.950195	2598257000
2021-09-20	^IXIC	14713.903320	14713.903320	14841.815430	14530.073242	14758.099609	4350941000

34549 rows × 7 columns

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategy, Trade Investment Manager, and Trade Asset Register Item

tsConfDat = TradeStrategyConfigData()

# SMA JSON Configuration
#
# Model Configuration:
#                win_fast   : fast moving average
#                win_slow   : slow moving average
# Signals to enter and exit a trade based on the fast ma crossing the slow ma

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 20, 'win_slow' : 100 } },
                            } } )

tsActionGen = TradeStrategyActionGenerator()
trade_asset = trade_assets[0]
    
# *** Trade Strategy Entry and Exit Rules based on SMA 

ts_unique_name = "ts_sma"
trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                         ts_marketdat_mng  = ts_marketdat_mng, 
                                         test_trade_asset  = trade_asset, 
                                         dirPathMarketData = dirPathMarketData,
                                         actionData        = tsConfDat )
# *** Trade Strategy Model

ts_model = TradeStrategyBaseModelPersist(\
                trade_asset               = trade_asset,
                ts_marketdat_mng          = ts_marketdat_mng,
                ts_enter                  = trade_enter_set,
                ts_exit                   = trade_exit_set,                       
                model_type                = ts_unique_name,
                model_version             = "1.0",
                market_data_period        = MarketDataPeriod.Day,
            ) 

# *** Trade Strategy Investment Manager 

taInvMng = TradingAssetInvMngBase(\
                                trade_asset                = trade_asset,
                                minPeriodsBetweenTrades    = 1,
                                initInvestment             = 1000000.0, 
                                capPercentToInvestPerTrade = 100.0,      #invest all funds avail in each trade
                                minInvestPercent           = 5.0,        
                                min_account_balance        = 1000.0 )

# *** Trade Register Item
# *** This object has all configuration defined to execute a given trade strategy,
# *** It can be executed individually in backtest or as part of a portfolio

ta_registered_item = TradingAssetRegisterItem(\
        trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
        ts_model                      = ts_model,
        tradingExecutionType          = TradingAssetExecutionType.Simulation,
        tradingBrokerObj              = brokerTradingObj,
        tradingAssetInvMngObj         = taInvMng )  

Display Entry Rules Generated SMA Signals

trade_enter_set.tsEntries[0].ts_model_pipline_mng.\
     getAllModelDataPipelineDataForStockSymbol(stockSymbol = "^IXIC" )

	Index	Adj Close	Close	High	Low	Open	Volume	sma_fast	sma_slow	sma_fast_gt_sma_slow	sma_fast_lt_sma_slow
Date
2000-05-24	^IXIC	3270.610107	3270.610107	3276.189941	3042.659912	3155.139893	2087980000	3599.450500	4167.194609	0	1
2000-05-25	^IXIC	3205.350098	3205.350098	3365.840088	3194.590088	3308.510010	1561840000	3571.016504	4157.936611	0	1
2000-05-26	^IXIC	3205.110107	3205.110107	3256.629883	3150.780029	3215.280029	1069440000	3538.239014	4150.970813	0	1
2000-05-30	^IXIC	3459.479980	3459.479980	3460.239990	3286.540039	3286.540039	1457360000	3513.309009	4146.790212	0	1
2000-05-31	^IXIC	3400.909912	3400.909912	3501.510010	3399.620117	3428.250000	1533730000	3494.082007	4143.528013	0	1
...	...	...	...	...	...	...	...	...	...	...	...
2021-09-14	^IXIC	15037.759766	15037.759766	15181.190430	15008.299805	15168.450195	4571950000	15060.770947	14363.204189	1	0
2021-09-15	^IXIC	15161.530273	15161.530273	15174.379883	14984.679688	15071.339844	4446270000	15086.038477	14374.651396	1	0
2021-09-16	^IXIC	15181.919922	15181.919922	15205.500000	15047.139648	15120.089844	3681700000	15118.838965	14385.082793	1	0
2021-09-17	^IXIC	15043.969727	15043.969727	15166.559570	14998.730469	15163.360352	6682650000	15143.947949	14394.620293	1	0
2021-09-20	^IXIC	14713.903320	14713.903320	14841.815430	14530.073242	14758.099609	4350941000	15143.910107	14401.249023	1	0

5365 rows × 11 columns

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Nasdaq ETF - SMA Trade Strategy",
                    timePeriod = DatasetDef(datasetType = DatasetType.Testing,
                                            strBegDate = "2001-01-01", 
                                            strEndDate = "2005-12-31" ),
                    trade_asset_registered_item = ta_registered_item )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()

	TA_Reg_Name	TA_Reg_Id	Inv_Mng_Id	Inv_Mng_Parent_Id	Is_Portfolio	Num_Trades_Executing	Num_Trades_Completed	Init_Capital	Final_Capital	Final_Inv_Capital	Cum_Return	Backtest_Status
0	ts_sma_^IXIC	None	None	None	No	0	22	1000000.0	837290.360474	0.0	-0.16271	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	ts_sma_^IXIC	No	ts_sma	^IXIC	2001-01-03	2005-12-30	1.0	-0.16	-0.03	-0.01	-0.48	0.08	-0.14

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0
ta_reg_item_name	ts_sma_^IXIC
ta_reg_item_id	None
is_portfolio	No
model_type	ts_sma
trade_asset	^IXIC
test_period_len_days	1825
tr_period_len_days	0
val_period_len_days	0
index	Strategy
Start Period	2001-01-03
End Period	2005-12-30
Risk-Free Rate	0.0
Time in Market	1.0
Cumulative Return	-0.16
CAGR%	-0.03
Sharpe	-0.01
Sortino	-0.01
Sortino/√2	-0.01
Max Drawdown	-0.48
Longest DD Days	1730
Volatility (ann.)	0.26
Calmar	-0.07
Skew	-0.42
Kurtosis	6.01
Expected Daily %	-0.0
Expected Monthly %	-0.0
Expected Yearly %	-0.03
Kelly Criterion	-0.0
Risk of Ruin	0.0
Daily Value-at-Risk	-0.03
Expected Shortfall (cVaR)	-0.03
Gain/Pain Ratio	-0.0
Gain/Pain (1M)	-0.01
Payoff Ratio	1.03
Profit Factor	1.0
Common Sense Ratio	0.95
CPC Index	0.51
Tail Ratio	0.95
Outlier Win Ratio	3.59
Outlier Loss Ratio	3.5
MTD	-0.01
3M	-0.08
6M	-0.06
YTD	-0.13
1Y	-0.13
3Y (ann.)	-0.02
5Y (ann.)	-0.03
10Y (ann.)	-0.03
All-time (ann.)	-0.03
Best Day	0.08
Worst Day	-0.14
Best Month	0.34
Worst Month	-0.21
Best Year	0.27
Worst Year	-0.15
Avg. Drawdown	-0.1
Avg. Drawdown Days	199
Recovery Factor	-0.34
Ulcer Index	1.02
Avg. Up Month	0.06

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Cottons Futures - Quandal Market Data - SMA Trade Strategy

• SMA Trade Strategy for Cottons Future

  Description:

    1. $1.0M Budget
    2. 100% capital invested
  
  

  Trade Strategies:

    1. One trade strategy: Cotton's Futures using Simple Moving Average(SMA) for entering/exiting a trade
  
  

  Backtest:

    1. Type: regular
    2. Test Period: 
        1. Begin Date = 2002-01-01 
        2. End Date   = 2005-12-31 

Market Data Manager

Trade Assets - Quandal

Download the following data from Quandal:

1. CHRIS/ICE_CT1

   Where CHRIS is the Quandal Data Provider, and the name after the / is the Futures symbol.

   The format of the JSON to download from Quandal is:

   { quandal-path :

      [ description of data, 
        [ columns to extract from the returned Quandal DataFrame ], 
        { "Quandal_Column" : "Rename Quandal Column" } 
      ]
   

   }

Note:

1. The nested dictionary to rename the original downloaded Quandal data, it is provided since Quandal does
   not have standard output column names.  
       - Futures data from different data sources might have different returned column names for the same
         state, that is "Close" vs. "close", etc.
       - For sharing the same market data manager object it is recommended to rename the columns to have 
         standard names, and match the ones provided by Yahoo Finance

2. Yahoo Finance returns standard equity column names

3. All implemented AI and Technical Models used standard column names (same as Yahoo Finance)
   Columns: Adj Close, Close, Open, High, Low, Volume
   Index: Datetime

4. In order to use the implemented models the Quandal Market Data or any Custom Market Data must have
   similar named columns, or the user can implement his own models and use whatever column names he/she
   prefers.

5. In this example, the Quandal data source does not provide all standard column names, only open,
   which is re-named to 'Adj Close'.  Therefore only some of the built models will be available,
   as they required High, Low, such as Bollinger Bands

indexQuantCodeMap = { 
       "CHRIS/ICE_CT1" : ["Cotton",   ["Open", "Volume"],  { "Open" : "Adj Close" } ] 
}

dirPathMarketDataQuandl = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/TSMarketDat'

beg_date_for_feature_creation = "2000-10-01"
beg_date_str                  = "2001-01-01"
end_date_str                  = "2010-12-31"

q_stock_index_list = list ( indexQuantCodeMap.keys()  )

ts_marketdat_mng_quandal = TradeStrategyMarketDataMng(\
        marketDataProviderName = "quandl",
        supportedTradeAssets   = q_stock_index_list,   
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "Quandl_Test_1",
        dirPathMarketData      = dirPathMarketDataQuandl,
        beg_date_str           = beg_date_for_feature_creation,
        end_date_str           = end_date_str,
        interval               = "1d" ,
        updateDataPassEndDate  = False,
        forceCreateMarketData  = False,
        indexQuantCodeMap      = indexQuantCodeMap     # required for quandl market data provider, defined above
        ) 

2021-11-02 09:26:33,287 [INFO] root: Found previous Market data previously loaded to specified file and dir.

ts_marketdat_mng_quandal.marketDataObj.getDataFrame()

	Index	Index_Name	Provider	Adj Close	Volume
Date
2000-10-02	CHRIS/ICE_CT1	Cotton	Quandl	62.17	40.0
2000-10-03	CHRIS/ICE_CT1	Cotton	Quandl	63.40	120.0
2000-10-04	CHRIS/ICE_CT1	Cotton	Quandl	63.70	37.0
2000-10-05	CHRIS/ICE_CT1	Cotton	Quandl	63.80	21.0
2000-10-06	CHRIS/ICE_CT1	Cotton	Quandl	63.95	84.0
...	...	...	...	...	...
2010-12-27	CHRIS/ICE_CT1	Cotton	Quandl	147.12	7893.0
2010-12-28	CHRIS/ICE_CT1	Cotton	Quandl	146.31	4413.0
2010-12-29	CHRIS/ICE_CT1	Cotton	Quandl	143.86	6900.0
2010-12-30	CHRIS/ICE_CT1	Cotton	Quandl	140.00	10309.0
2010-12-31	CHRIS/ICE_CT1	Cotton	Quandl	144.00	5421.0

2401 rows × 5 columns

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategy, Trade Investment Manager, and Trade Asset Register Item

tsConfDat = TradeStrategyConfigData()

# SMA JSON Configuration

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 20, 'win_slow' : 100 } },
                            } } )

tsActionGen = TradeStrategyActionGenerator()
trade_asset = "CHRIS/ICE_CT1"
    
# *** Trade Strategy Entry and Exit Rules based on SMA 

ts_unique_name = "ts_sma"
trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                         ts_marketdat_mng  = ts_marketdat_mng_quandal, 
                                         test_trade_asset  = trade_asset, 
                                         dirPathMarketData = dirPathMarketData,
                                         actionData        = tsConfDat )
# *** Trade Strategy Model

ts_model = TradeStrategyBaseModelPersist(\
                trade_asset               = trade_asset,
                ts_marketdat_mng          = ts_marketdat_mng_quandal,
                ts_enter                  = trade_enter_set,
                ts_exit                   = trade_exit_set,                       
                model_type                = ts_unique_name,
                model_version             = "1.0",
                market_data_period        = MarketDataPeriod.Day,
            ) 

# *** Trade Strategy Investment Manager 

taInvMng = TradingAssetInvMngBase(\
                                trade_asset                = trade_asset,
                                minPeriodsBetweenTrades    = 1,
                                initInvestment             = 1000000.0, 
                                capPercentToInvestPerTrade = 100.0,      #invest all funds avail in each trade
                                minInvestPercent           = 5.0,        
                                min_account_balance        = 1000.0 )

# *** Trade Register Item
# *** This object has all configuration defined to execute a given trade strategy,
# *** It can be executed individually in backtest or as part of a portfolio

ta_registered_item = TradingAssetRegisterItem(\
        trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
        ts_model                      = ts_model,
        tradingExecutionType          = TradingAssetExecutionType.Simulation,
        tradingBrokerObj              = brokerTradingObj,
        tradingAssetInvMngObj         = taInvMng )  

Display Entry Rules Generated SMA Signals

trade_enter_set.tsEntries[0].ts_model_pipline_mng.\
     getAllModelDataPipelineDataForStockSymbol(stockSymbol = trade_asset )

	Index	Index_Name	Provider	Adj Close	Volume	sma_fast	sma_slow	sma_fast_gt_sma_slow	sma_fast_lt_sma_slow
Date
2001-02-26	CHRIS/ICE_CT1	Cotton	Quandl	55.25	49.0	59.3340	62.6098	0	1
2001-02-27	CHRIS/ICE_CT1	Cotton	Quandl	53.50	176.0	58.9390	62.5231	0	1
2001-02-28	CHRIS/ICE_CT1	Cotton	Quandl	51.95	166.0	58.4860	62.4086	0	1
2001-03-01	CHRIS/ICE_CT1	Cotton	Quandl	53.10	46.0	58.0310	62.3026	0	1
2001-03-02	CHRIS/ICE_CT1	Cotton	Quandl	53.75	83.0	57.6685	62.2021	0	1
...	...	...	...	...	...	...	...	...	...
2010-12-27	CHRIS/ICE_CT1	Cotton	Quandl	147.12	7893.0	136.7070	108.9030	1	0
2010-12-28	CHRIS/ICE_CT1	Cotton	Quandl	146.31	4413.0	137.5775	109.5818	1	0
2010-12-29	CHRIS/ICE_CT1	Cotton	Quandl	143.86	6900.0	138.0755	110.2359	1	0
2010-12-30	CHRIS/ICE_CT1	Cotton	Quandl	140.00	10309.0	138.7005	110.8444	1	0
2010-12-31	CHRIS/ICE_CT1	Cotton	Quandl	144.00	5421.0	139.8280	111.4864	1	0

2302 rows × 9 columns

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Cottons Futures - SMA Trade Strategy",
                    timePeriod = DatasetDef(datasetType = DatasetType.Testing,
                                            strBegDate = "2002-01-01", 
                                            strEndDate = "2005-12-31" ),
                    trade_asset_registered_item = ta_registered_item )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()

	TA_Reg_Name	TA_Reg_Id	Inv_Mng_Id	Inv_Mng_Parent_Id	Is_Portfolio	Num_Trades_Executing	Num_Trades_Completed	Init_Capital	Final_Capital	Final_Inv_Capital	Cum_Return	Backtest_Status
0	ts_sma_CHRIS/ICE_CT1	None	None	None	No	0	13	1000000.0	942553.57	0.0	-0.057446	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	ts_sma_CHRIS/ICE_CT1	No	ts_sma	CHRIS/ICE_CT1	2002-01-03	2005-12-30	0.98	-0.06	-0.01	0.12	-0.38	0.19	-0.09

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0
ta_reg_item_name	ts_sma_CHRIS/ICE_CT1
ta_reg_item_id	None
is_portfolio	No
model_type	ts_sma
trade_asset	CHRIS/ICE_CT1
test_period_len_days	1460
tr_period_len_days	0
val_period_len_days	0
index	Strategy
Start Period	2002-01-03
End Period	2005-12-30
Risk-Free Rate	0.0
Time in Market	0.98
Cumulative Return	-0.06
CAGR%	-0.01
Sharpe	0.12
Sortino	0.19
Sortino/√2	0.13
Max Drawdown	-0.38
Longest DD Days	571
Volatility (ann.)	0.34
Calmar	-0.04
Skew	1.23
Kurtosis	10.68
Expected Daily %	-0.0
Expected Monthly %	-0.0
Expected Yearly %	-0.01
Kelly Criterion	0.01
Risk of Ruin	0.0
Daily Value-at-Risk	-0.03
Expected Shortfall (cVaR)	-0.03
Gain/Pain Ratio	0.02
Gain/Pain (1M)	0.09
Payoff Ratio	1.08
Profit Factor	1.02
Common Sense Ratio	1.06
CPC Index	0.54
Tail Ratio	1.04
Outlier Win Ratio	3.77
Outlier Loss Ratio	3.61
MTD	-0.07
3M	-0.1
6M	-0.18
YTD	-0.31
1Y	-0.3
3Y (ann.)	-0.0
5Y (ann.)	-0.01
10Y (ann.)	-0.01
All-time (ann.)	-0.01
Best Day	0.19
Worst Day	-0.09
Best Month	0.21
Worst Month	-0.2
Best Year	0.22
Worst Year	-0.31
Avg. Drawdown	-0.09
Avg. Drawdown Days	94
Recovery Factor	-0.15
Ulcer Index	1.02
Avg. Up Month	0.08

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Nasdaq ETF - Yahoo Finance Market Data - LSTM Trade Strategy

• LSTM Trade Strategy for Nasdaq ETF

  Description:

    1. $1.0M Budget
    2. 100% capital invested
  
  

  Trade Strategies:

    1. One trade strategy: Nasdaq using a LSTM-Model for entering/exiting a trade
  
  

  Backtest:

    1. Type: regular
    2. AI Models Fit Date Ranges
        1. Training Period: 
            1. Begin Date = 2001-01-01 
            2. End Date   = 2005-12-31  
        2. Validation Period: 
            1. Begin Date = 2006-01-01 
            2. End Date   = 2007-06-30   
    4. Test Period: 
        1. Begin Date = 2008-01-01 
        2. End Date   = 2008-12-31 

Trade Asset

trade_assets   = [ "^IXIC" ]

Market Data Manager

Market Data Manager configured to use yahoo-finance for obtaining market data

The following TradeStrategyMarketDataMng object will do the following:

1. Download market data for: 
    [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
2. Used yahoo-finance as the market provider
3. Save the downloaded data in the directory and file specified by the arguments:
   1. dirPathMarketData
   2. fileNameMarketData
4. It will request the market data provider to get data from "beg_date_str" to "end_date_str"
5. If end_date_str is not defined (as the case below), all data available from the begin date is 
   requested.
6. Interval is set to "1d", this parameter is market data provider specific, see Yahoo-Finance, 
   Quandal for what options they support
7. The **Adj Close** column is configured as the default price column 
8. Note that even so leveraged ETFs are requested from 2000-01-01 to the present, the data 
   actually returned for these stock symbols by Yahoo-Finance doesn't start until 2011.
   The behavior is market data provider specific

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-11-02 09:29:25,029 [INFO] root: Found previous Market data previously loaded to specified file and dir.

ts_marketdat_mng.marketDataObj.getDataFrame()

	Index	Adj Close	Close	High	Low	Open	Volume
Date
2000-01-03	^DJI	11357.509766	11357.509766	11522.009766	11305.690430	11501.849609	169750000
2000-01-03	^GSPC	1455.219971	1455.219971	1478.000000	1438.359985	1469.250000	931800000
2000-01-03	^IXIC	4131.149902	4131.149902	4192.189941	3989.709961	4186.189941	1510070000
2000-01-04	^DJI	10997.929688	10997.929688	11350.059570	10986.450195	11349.750000	178420000
2000-01-04	^GSPC	1399.420044	1399.420044	1455.219971	1397.430054	1455.219971	1009000000
...	...	...	...	...	...	...	...
2021-09-20	TQQQ	133.490005	133.490005	137.460007	128.000000	135.419998	60721113
2021-09-20	UDOW	69.760002	69.760002	71.459999	67.519997	70.220001	6439708
2021-09-20	^DJI	33970.468750	33970.468750	34459.718750	33613.031250	34459.718750	441645657
2021-09-20	^GSPC	4357.729980	4357.729980	4402.950195	4305.910156	4402.950195	2598257000
2021-09-20	^IXIC	14713.903320	14713.903320	14841.815430	14530.073242	14758.099609	4350941000

34549 rows × 7 columns

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategy, Trade Investment Manager, and Trade Asset Register Item

tsConfDat = TradeStrategyConfigData()

# LSTM JSON Configuration
#
# Model configuration:
#     Simple LSTM MOdel with two layers
#     n_steps            : length of time series
#     no_neurons_lay1    : layer 1 neurons
#     no_neurons_lay2    : layer 2 neurons
#     pred_ahead_periods : Number of prediction points ahead
#     epochs             : Number of epochs
#     ext_trade_dyn      : Extend an existent trade dymically based on current period predictions
#     re_eval_exit_dyn   : Re-evaluate exiting a trade dynamically based on current period predictions
#     entry_policy_args  :
#                          lastn_match        : Enter a trade if the last n predictions matched
#                          cross_enter_signal : Enter a trade if the there is a cross-over signal, 
#                                               from buy-to-sell, sell-to-buy
#     exit_policy_args   :
#                          lastn_match        : Exit a trade if the last n predictions matched


tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

tsActionGen = TradeStrategyActionGenerator()
trade_asset = "^IXIC"
    
# *** Trade Strategy Entry and Exit Rules based on SMA 

ts_unique_name = "ts_lstm"
trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                         ts_marketdat_mng  = ts_marketdat_mng, 
                                         test_trade_asset  = trade_asset, 
                                         dirPathMarketData = dirPathMarketData,
                                         actionData        = tsConfDat )
# *** Trade Strategy Model

ts_model = TradeStrategyBaseModelPersist(\
                trade_asset               = trade_asset,
                ts_marketdat_mng          = ts_marketdat_mng,
                ts_enter                  = trade_enter_set,
                ts_exit                   = trade_exit_set,                       
                model_type                = ts_unique_name,
                model_version             = "1.0",
                market_data_period        = MarketDataPeriod.Day,
            ) 

# *** Trade Strategy Investment Manager 

taInvMng = TradingAssetInvMngBase(\
                                trade_asset                = trade_asset,
                                minPeriodsBetweenTrades    = 1,
                                initInvestment             = 1000000.0, 
                                capPercentToInvestPerTrade = 100.0,      #invest all funds avail in each trade
                                minInvestPercent           = 5.0,        
                                min_account_balance        = 1000.0 )

# *** Trade Register Item
# *** This object has all configuration defined to execute a given trade strategy,
# *** It can be executed individually in backtest or as part of a portfolio

ta_registered_item = TradingAssetRegisterItem(\
        trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
        ts_model                      = ts_model,
        tradingExecutionType          = TradingAssetExecutionType.Simulation,
        tradingBrokerObj              = brokerTradingObj,
        tradingAssetInvMngObj         = taInvMng )  

Display Pipeline Data for LSTM Model

# Pipeline just normalizes the data
trade_enter_set.tsEntries[0].ts_model_pipline_mng.\
     getAllModelDataPipelineDataForStockSymbol(stockSymbol = "^IXIC" )

	Adj Close	Close	High	Low	Open	Volume	Index
Date
2000-01-03	0.211570	0.211570	0.214213	0.202407	0.215259	0.124230	^IXIC
2000-01-04	0.195479	0.195479	0.205877	0.195980	0.203604	0.124392	^IXIC
2000-01-05	0.193786	0.193786	0.195431	0.184504	0.191987	0.144828	^IXIC
2000-01-06	0.183238	0.183238	0.191544	0.183152	0.190591	0.132287	^IXIC
2000-01-07	0.194142	0.194142	0.192519	0.182834	0.181941	0.135630	^IXIC
...	...	...	...	...	...	...	...
2021-09-14	0.976398	0.976398	0.984423	0.976467	0.985446	0.403784	^IXIC
2021-09-15	0.985077	0.985077	0.983945	0.974808	0.978636	0.392309	^IXIC
2021-09-16	0.986507	0.986507	0.986127	0.979196	0.982054	0.322503	^IXIC
2021-09-17	0.976833	0.976833	0.983397	0.975795	0.985089	0.596493	^IXIC
2021-09-20	0.953687	0.953687	0.960636	0.942872	0.956668	0.383605	^IXIC

5464 rows × 7 columns

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2000-07-01",
                             strBegDate         = "2001-01-01",
                             strEndDate         = "2005-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-06-30" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2008-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Nasdaq-ETF LSTM based Trade Strategy",
                    timePeriod  = testing_period,
                    fitDatasets = fit_datasets,
                    trade_asset_registered_item = ta_registered_item )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()

	TA_Reg_Name	TA_Reg_Id	Inv_Mng_Id	Inv_Mng_Parent_Id	Is_Portfolio	Num_Trades_Executing	Num_Trades_Completed	Init_Capital	Final_Capital	Final_Inv_Capital	Cum_Return	Backtest_Status
0	ts_lstm_^IXIC	None	None	None	No	0	56	1000000.0	2.746124e+06	0.0	1.746124	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2008-01-03	2008-12-31	1.0	1.75	1.76	2.67	-0.23	0.12	-0.07

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0
ta_reg_item_name	ts_lstm_^IXIC
ta_reg_item_id	None
is_portfolio	No
model_type	ts_lstm
trade_asset	^IXIC
test_period_len_days	365
tr_period_len_days	1825
val_period_len_days	545
index	Strategy
Start Period	2008-01-03
End Period	2008-12-31
Risk-Free Rate	0.0
Time in Market	1.0
Cumulative Return	1.75
CAGR%	1.76
Sharpe	2.67
Sortino	4.81
Sortino/√2	3.4
Max Drawdown	-0.23
Longest DD Days	54
Volatility (ann.)	0.41
Calmar	7.58
Skew	0.81
Kurtosis	2.75
Expected Daily %	0.0
Expected Monthly %	0.09
Expected Yearly %	1.75
Kelly Criterion	0.22
Risk of Ruin	0.0
Daily Value-at-Risk	-0.04
Expected Shortfall (cVaR)	-0.04
Gain/Pain Ratio	0.62
Gain/Pain (1M)	6.99
Payoff Ratio	1.22
Profit Factor	1.62
Common Sense Ratio	2.3
CPC Index	1.13
Tail Ratio	1.42
Outlier Win Ratio	4.7
Outlier Loss Ratio	3.26
MTD	0.26
3M	0.23
6M	0.97
YTD	1.75
1Y	1.75
3Y (ann.)	1.76
5Y (ann.)	1.76
10Y (ann.)	1.76
All-time (ann.)	1.76
Best Day	0.12
Worst Day	-0.07
Best Month	0.44
Worst Month	-0.14
Best Year	1.75
Worst Year	1.75
Avg. Drawdown	-0.04
Avg. Drawdown Days	8
Recovery Factor	7.51
Ulcer Index	1.45
Avg. Up Month	0.15

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Portfolios

Portfolio A

1. Portfolio A: Long and SMA Trade Strategies for US Indixes

   Description:

    1. Portfolio with 6 trading strategies
    2. $1.2M Budget
    3. No-Rebalance during the backtest 
    4. 100% capital invested
    5. Portfolio investment using equal weights for each trade strategy
   
   

   Trade Strategies:

    1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold
    2. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using SMA 
   
   

   Backtest:

    1. Type: regular
    2. Test Period: 
        1. Begin Date = 2001-01-01 
        2. End Date = 2001-12-31 

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-27 11:05:58,057 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

Define Indicators & Signals

This is a second more detail approach to built a SMA trade strategy versus the example SMA Test Trade Strategy

• Use this approach if you want total control of the rules, implement your own. The other approach just wraps this JSON and the other models built-in in a wrapper function to do most of the plumming.
• Define the rules to create the SMA signals via JSON dict
• Used a 20-day SMA and a 100-day SMA for signaled-generation
• The Adj Close column from the market data will be used for the calculations
• The following open source library is leveraged for the SMA calculations: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html#trend-indicators
• The configuration of the library and method invocation is specified dynamically via the inputs:
  1. package
  2. class_name
  3. input_param_series
  4. input_param_series_mkd_col
  5. input_params_other Note:
     • input_param_series list of inputs are mapped to the input_param_series_mkd_col list of columns, which are extracted from the Market Data.
     • input_params_other are additional inputs the class takes in the constructor (see doc reference)
• The output is generated by the method specified by output_generating_funcs, a Pandas Series is expected for the return from the method (the standard usage in the technical analysis library )
• The output return Pandas Series is stored in a dataframe column in the pipeline object, it is specified via output_pipeline_cols
• Multiple methods can be specified and multiple pipeline output columns, for SMA is not necessary, but for other indicators such as Bollinger Bands necessary
• The rules for creating the SMA signals are defined via the "signals" part of the JSON. For SMA is just a matter of expressing the relation between the fast and slow moving averages

json_dat = {
    "version"    : "1.0",
    
    "indicators" : {
        
        "SMA" :  {
            "ta_indicators" : {
                  "sma_20": {
                      "package"                   : "ta.trend",
                      "class_name"                : "SMAIndicator",
                      "input_param_series"        : ["close"],
                      "input_param_series_mkd_col": ["Adj Close"],
                      "input_params_other"        : { "window": 20, "fillna": False },
                      "output_pipeline_cols"      : [ "sma_20" ],
                      "output_generating_funcs"   : [ "sma_indicator()" ]
                   },

                   "sma_100": {
                      "package"                   : "ta.trend",
                      "class_name"                : "SMAIndicator",
                      "input_param_series"        : ["close"],
                      "input_param_series_mkd_col": ["Adj Close"],
                      "input_params_other"        : { "window": 100, "fillna": False },
                      "output_pipeline_cols"      : [ "sma_100" ],
                      "output_generating_funcs"   : [ "sma_indicator()" ]
                   }   
            },     

            "signals" : {
                  "sma20_gt_sma100": {
                      "columns"       : [ "sma_20", "sma_100" ],
                      "relationship"  : "gt",
                      "signal_col"    : "sma20_gt_sma100"
                   },
                
                   "sma20_lt_sma100": {
                      "signal_gen_func" : "signalSampleCreator",
                      "func_args"       : { },
                      "signal_col"      : "sma20_lt_sma100"
                   },         
            }             
        },
    },
}

def signalSampleCreator(df : pd.DataFrame, **kwargs ) -> pd.core.series.Series :
    return df["sma_20"] < df["sma_100"]

Define Pipeline Manager

taPipilineObj = TALibPipelineBuilder( trade_assets = portfolio_trade_assets, 
                                      json_dat = json_dat, 
                                      signalgen_func_dict = { 'signalSampleCreator' : signalSampleCreator } )

ts_model_pipline_mng = TradeStrategyModelPipelineDataMng(\
                        tsMarketDataMng         = ts_marketdat_mng,
                        modelPipelineBuilder    = taPipilineObj,
                        fileNameMarketData      = "US_Indixes_And_ETFs_PipelineData",
                        forceCreatePipelineData = True,
                        dirPathMarketData       = dirPathMarketData ) 

dfx = ts_model_pipline_mng.getAllModelDataPipelineDataForStockSymbol(stockSymbol = "^GSPC" )
dfx

	Index	Adj Close	Close	High	Low	Open	Volume	sma_20	sma_100	sma20_gt_sma100	sma20_lt_sma100
Date
2000-05-24	^GSPC	1399.050049	1399.050049	1401.750000	1361.089966	1373.859985	1152300000	1426.065509	1428.297998	0	1
2000-05-25	^GSPC	1381.520020	1381.520020	1411.650024	1373.930054	1399.050049	984500000	1421.895508	1427.560999	0	1
2000-05-26	^GSPC	1378.020020	1378.020020	1391.420044	1369.750000	1381.520020	722600000	1418.175006	1427.346998	0	1
2000-05-30	^GSPC	1422.449951	1422.449951	1422.449951	1378.020020	1378.020020	844200000	1415.885004	1427.550398	0	1
2000-05-31	^GSPC	1420.599976	1420.599976	1434.489990	1415.500000	1422.439941	960500000	1414.600500	1427.721898	0	1
...	...	...	...	...	...	...	...	...	...	...	...
2021-09-14	^GSPC	4443.049805	4443.049805	4485.680176	4435.459961	4479.330078	2568730000	4484.140967	4318.467500	1	0
2021-09-15	^GSPC	4480.700195	4480.700195	4486.870117	4438.370117	4447.490234	3154760000	4485.771973	4321.472803	1	0
2021-09-16	^GSPC	4473.750000	4473.750000	4485.870117	4443.799805	4477.089844	3321030000	4489.445972	4324.334102	1	0
2021-09-17	^GSPC	4432.990234	4432.990234	4471.520020	4427.759766	4469.740234	5622210000	4490.805493	4326.796802	1	0
2021-09-20	^GSPC	4357.729980	4357.729980	4402.950195	4305.910156	4402.950195	2598257000	4486.608496	4328.542300	1	0

5365 rows × 11 columns

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

# Conducting a backtest for a Trade Strategy requires:
#
#    1. A Trade Strategy Model 
#         - A TS Model is associated with only on trade asset 
#         - References a Market Data Manager 
#         - Trading Rules: Entry, Exit,Stop-Loss, Trail-Stop-Loss, etc.  (only Entry rules are required)
#         - Each Trading Action may have a Pipeline Data Manager associated with. 
#           - For example: For using SMA to enter/exit trades, the signasl must be computed based on the
#                        Market Data and SMA indicators/signals configured
#           - Different trade actions might share the same pipeline manager or create different onces
#         - Each Trading Action has their own configuration/metadata
#           - Entry Rules: 
#               Decide how to combine signals and which signals to enter/exit
#               Entry Policies:  
#                  - Only enter a trade if the last n dates/periods were marked for entering a trade
#                  - Only enter a trade if there is a cross-over signal (from buy to sell, or sell to buy)
#               More complex configuration options are availabe for creating entry rules based on an ensemble
#               of entry rule algorithms. This will be explained and detail in other examples.
#           
#    2. A Broker to conduct the trades with 
#         - can be shared across Trade Strategies, but no required
#
#    3. A Trade Strategy Investment Manager
#         - Manages the budget and spending rules (max/min to invest)
#         - Trades 
#         - Interfaces with Broker objects to place, cancel, alter trades
#         - Other investment metadata rules
#         - Works with Portfolio Investment Managers to rebalance funds across a portfolio
#
#    4. A Trade Asset Register Item
#         - This is a compose object to have all necessary information to conduct a backtest 
#           either as a single item, or as part of a portfolio
#         - The dates and characteristics of the backtest execution are defined separately


# Each Trade Register Item Object has:
#   - A Name 
#   - One Trade Strategy
#       + Each Trade Strategy has one or more actions defined (only entry rules are mandatory)
#           a) Entry Rules
#           b) Exit Rules
#           c) Stop Loss and/or Trail Stop Rules
#           d) Position Sizing Rules per trade 
#           etc.
#   - One Investment Manager
#   - A broker to trade with (simulation, or paper trading)

taRegisterItems = [ ]

for trade_asset in portfolio_trade_assets :
    
    # *** Trading Rules Model, generating buy, sell, hold trade decisions based on long/short signals
    # *** N number of signals can be configured to entered long or short trades, the 
    # *** long_signals_bool_logic or short_signals_bool_logic control determine logic to apply
    # *** See also: VotingTradingRulesModel() to derive rules based on voting metadata
    
    classic_model_enter = TradingRulesModel( name = "SMA_Enter",
                                       enter_long_signals  = [ "sma20_gt_sma100" ],
                                       enter_short_signals = [ "sma20_lt_sma100" ],
                                       long_signals_bool_logic  =  "and",  
                                       short_signals_bool_logic  =  "and"   ) 
    
    classic_model_exit = TradingRulesModel( name = "SMA_Exit",
                                       enter_long_signals  = [ "sma20_gt_sma100" ],
                                       enter_short_signals = [ "sma20_lt_sma100" ],
                                       long_signals_bool_logic  =  "and",  
                                       short_signals_bool_logic  =  "and"   ) 
    
    # *** TS Entry Rules 
    # *** Uses Trading Rules Model configured above to determine when to enter a trade
    
    ts_entry = TradeEnterClassicStrategy( 
                         classic_trading_model  = CacheTradingModel( name = "SMA_20_AND_100_Enter",
                                                                     classic_model = classic_model_enter ),
                         ts_model_pipline_mng   = ts_model_pipline_mng )  
 
    # *** TS Exit Rules 
    # *** Uses Trading Rules Model configured above to determine when to exit a trade,
    # *** The exit rules can used a different set of signals (EMA, BB, or AI based rules) than entry rules,
    # *** in this example the entry and exit rules are based on the same set of SMA trading rules
    
    ts_exit = TradeExitClassicStrategy( 
                         classic_trading_model  = CacheTradingModel( name = "SMA_20_AND_100_Exit",
                                                                     classic_model = classic_model_exit ),
                         ts_model_pipline_mng   = ts_model_pipline_mng )  
    
    # *** Trade Strategy 
    
    ts_model = TradeStrategyBaseModelPersist(\
                    trade_asset               = trade_asset,
                    ts_marketdat_mng          = ts_marketdat_mng,
                    ts_enter                  = TradeEnterStack( tsEntries = [ ts_entry ] ),
                    ts_exit                   = TradeExitStack( tsExits = [ ts_exit ] ),                    
                    model_type                = "sma",
                    model_version             = "1.0",
                    market_data_period        = MarketDataPeriod.Day,
                ) 
    
    # *** Trade Strategy Investment Manager 
    
    taInvMng = TradingAssetInvMngBase(\
                                    trade_asset                = trade_asset,
                                    minPeriodsBetweenTrades    = 1,
                                    initInvestment             = 1000000.0, 
                                    capPercentToInvestPerTrade = 100.0,
                                    minInvestPercent           = 5.0,
                                    min_account_balance        = 1000.0 )

    # *** Trade Register Item
    # *** This object has all configuration defined to execute a given trade strategy,
    # *** It can be executed individually in backtest or as part of a portfolio
    
    ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "TA - SMA for: {0}".format(trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  
 
    taRegisterItems.append(ta_registered_item)
    

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - SMA Algo",
                  initInvestment             = 12000000.0, 
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                            name                 = "Portfolio A, US-Indexes using SMA and Long strategies",
                            taRegisterItems      = taRegisterItems,
                            portfolioInvMngObj   = portfInvMng,
                            tradingExecutionType = TradingAssetExecutionType.Simulation,
                            add_long_only_ts     = True )

2021-10-27 11:06:13,163 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio A, US-Indexes using SMA and Long strategies" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

2021-10-27 11:06:20,847 [INFO] root: PortfolioRegisterItem init called. 
Portfolio of US-Indexes using SMA and Long strategies
1 Portfolio of US-Indexes using SMA and Long strategies

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for SMA based Trade Strategies",
                    timePeriod = DatasetDef(datasetType = DatasetType.Testing,
                                            strBegDate = "2001-01-01", 
                                            strEndDate = "2001-12-31" ),
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()

	TA_Reg_Name	TA_Reg_Id	Inv_Mng_Id	Inv_Mng_Parent_Id	Is_Portfolio	Num_Trades_Executing	Num_Trades_Completed	Init_Capital	Final_Capital	Final_Inv_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes using SMA and Long str...	1	1	NaN	Yes	0	21	12000000.0	1.082878e+07	0.0	-0.097602	Completed
1	TA - SMA for: ^DJI	1	1	1.0	No	0	6	2000000.0	1.871721e+06	0.0	-0.064139	Completed
2	TA - SMA for: ^IXIC	2	2	1.0	No	0	8	2000000.0	1.720096e+06	0.0	-0.139952	Completed
3	TA - SMA for: ^GSPC	3	3	1.0	No	0	4	2000000.0	1.862138e+06	0.0	-0.068931	Completed
4	Long-Only Trade Strategy for ^GSPC	4	4	1.0	No	0	1	2000000.0	1.789374e+06	0.0	-0.105313	Completed
5	Long-Only Trade Strategy for ^IXIC	5	5	1.0	No	0	1	2000000.0	1.702247e+06	0.0	-0.148877	Completed
6	Long-Only Trade Strategy for ^DJI	6	6	1.0	No	0	1	2000000.0	1.883204e+06	0.0	-0.058398	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio of US-Indexes using SMA and Long str...	Yes			2001-01-03	2001-12-31	0.79	-0.1	-0.1	-1.26	-0.13	0.03	-0.03
1	TA - SMA for: ^DJI	No	sma	^DJI	2001-01-03	2001-12-31	1.0	-0.06	-0.06	-0.25	-0.2	0.06	-0.04
2	TA - SMA for: ^IXIC	No	sma	^IXIC	2001-01-03	2001-12-31	1.0	-0.14	-0.14	-0.19	-0.38	0.08	-0.14
3	TA - SMA for: ^GSPC	No	sma	^GSPC	2001-01-03	2001-12-31	1.0	-0.07	-0.07	-0.28	-0.24	0.04	-0.05
4	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2001-01-03	2001-12-31	1.0	-0.11	-0.11	-0.42	-0.3	0.05	-0.05
5	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2001-01-03	2001-12-31	1.0	-0.15	-0.15	-0.17	-0.5	0.14	-0.07
6	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2001-01-03	2001-12-31	1.0	-0.06	-0.06	-0.18	-0.27	0.04	-0.07

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0	1	2	3	4	5	6
ta_reg_item_name	Portfolio of US-Indexes using SMA and Long str...	TA - SMA for: ^DJI	TA - SMA for: ^IXIC	TA - SMA for: ^GSPC	Long-Only Trade Strategy for ^GSPC	Long-Only Trade Strategy for ^IXIC	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	1	1	2	3	4	5	6
is_portfolio	Yes	No	No	No	No	No	No
model_type		sma	sma	sma	long	long	long
trade_asset		^DJI	^IXIC	^GSPC	^GSPC	^IXIC	^DJI
test_period_len_days	364	364	364	364	364	364	364
tr_period_len_days	0	0	0	0	0	0	0
val_period_len_days	0	0	0	0	0	0	0
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03
End Period	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	0.79	1.0	1.0	1.0	1.0	1.0	1.0
Cumulative Return	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
CAGR%	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
Sharpe	-1.26	-0.25	-0.19	-0.28	-0.42	-0.17	-0.18
Sortino	-1.58	-0.36	-0.26	-0.39	-0.59	-0.25	-0.25
Sortino/√2	-1.12	-0.26	-0.19	-0.28	-0.42	-0.18	-0.18
Max Drawdown	-0.13	-0.2	-0.38	-0.24	-0.3	-0.5	-0.27
Longest DD Days	332	182	270	270	334	340	223
Volatility (ann.)	0.08	0.19	0.4	0.19	0.21	0.43	0.21
Calmar	-0.78	-0.32	-0.37	-0.29	-0.36	-0.3	-0.22
Skew	-0.64	0.32	-0.42	0.0	0.1	0.65	-0.47
Kurtosis	10.6	2.71	3.89	1.32	1.59	2.92	3.72
Expected Daily %	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0
Expected Monthly %	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01
Expected Yearly %	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
Kelly Criterion	-0.19	-0.02	-0.02	-0.02	-0.04	-0.01	-0.02
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.01	-0.02	-0.04	-0.02	-0.02	-0.04	-0.02
Expected Shortfall (cVaR)	-0.01	-0.02	-0.04	-0.02	-0.02	-0.04	-0.02
Gain/Pain Ratio	-0.28	-0.04	-0.03	-0.04	-0.07	-0.03	-0.03
Gain/Pain (1M)	-0.79	-0.18	-0.1	-0.15	-0.26	-0.1	-0.13
Payoff Ratio	0.74	0.96	1.08	0.98	1.0	0.95	0.97
Profit Factor	0.72	0.96	0.97	0.96	0.93	0.97	0.97
Common Sense Ratio	0.54	1.11	1.11	1.12	0.93	0.94	0.96
CPC Index	0.26	0.46	0.49	0.46	0.45	0.47	0.47
Tail Ratio	0.76	1.16	1.15	1.17	1.0	0.97	0.99
Outlier Win Ratio	6.68	3.49	3.19	3.02	3.82	3.6	3.67
Outlier Loss Ratio	5.09	3.52	2.99	3.18	3.23	2.94	3.6
MTD	0.01	0.03	0.01	0.01	0.01	0.01	0.02
3M	0.02	-0.05	-0.18	-0.07	0.1	0.3	0.13
6M	-0.03	0.09	-0.07	0.02	-0.06	-0.1	-0.05
YTD	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
1Y	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
3Y (ann.)	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
5Y (ann.)	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
10Y (ann.)	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
All-time (ann.)	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
Best Day	0.03	0.06	0.08	0.04	0.05	0.14	0.04
Worst Day	-0.03	-0.04	-0.14	-0.05	-0.05	-0.07	-0.07
Best Month	0.01	0.1	0.34	0.1	0.08	0.21	0.09
Worst Month	-0.04	-0.08	-0.21	-0.09	-0.09	-0.22	-0.11
Best Year	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
Worst Year	-0.1	-0.06	-0.14	-0.07	-0.11	-0.15	-0.06
Avg. Drawdown	-0.06	-0.09	-0.09	-0.05	-0.09	-0.15	-0.15
Avg. Drawdown Days	168	69	37	37	89	88	118
Recovery Factor	-0.77	-0.32	-0.37	-0.28	-0.35	-0.3	-0.21
Ulcer Index	1.11	1.07	1.04	1.04	1.04	1.02	1.08
Avg. Up Month	0.01	0.04	0.12	0.06	0.04	0.11	0.04

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Re-Store Backtest

session.expunge_all()
del tsExecuteEngine

for tsObj in session.query(BacktestExecution):  
    print(tsObj.name)
    if  tsObj.name  == "Backtest for SMA based Trade Strategies" :
        break

tsExecuteEngine = tsObj
print(tsExecuteEngine.id, tsExecuteEngine.name)

2021-10-27 11:07:52,246 [INFO] root: BacktestExecution init called. 
2021-10-27 11:07:52,255 [INFO] root: PortfolioRegisterItem init called. 
2021-10-27 11:07:52,256 [INFO] root: Loaded TradingAssetRegisterBase object
2021-10-27 11:07:52,266 [INFO] root: BacktestExecutionDatasets init called. 
Backtest for SMA based Trade Strategies
1 Backtest for SMA based Trade Strategies

tsExecuteEngine.getExecutionMetrics()

2021-10-27 11:07:52,374 [INFO] root: PortfolioInvMng init called. 
2021-10-27 11:07:52,375 [INFO] root: PortfolioInvMng init called. 
2021-10-27 11:07:52,392 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-27 11:07:52,393 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-27 11:07:52,393 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-27 11:07:52,394 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-27 11:07:52,395 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-27 11:07:52,396 [INFO] root: TradingAssetRegisterItem init called. 
**** TradingAssetInvMngBase init called. 
2021-10-27 11:07:52,403 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-27 11:07:52,422 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-27 11:07:52,441 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-27 11:07:52,458 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-27 11:07:52,476 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-27 11:07:52,492 [INFO] root: TradingAssetInvMngBase init called. 

/Users/gustavozambrana/venvs/CFRM505/lib/python3.7/site-packages/sqlalchemy/orm/loading.py:628: SAWarning: Loading context for <PortfolioInvMng at 0x7f9be8832a50> has changed within a load/refresh handler, suggesting a row refresh operation took place. If this event handler is expected to be emitting row refresh operations within an existing load or refresh operation, set restore_load_context=True when establishing the listener to ensure the context remains unchanged when the event handler completes.
  "handler completes." % (state_str(state),)

	TA_Reg_Name	TA_Reg_Id	Inv_Mng_Id	Inv_Mng_Parent_Id	Is_Portfolio	Num_Trades_Executing	Num_Trades_Completed	Init_Capital	Final_Capital	Final_Inv_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes using SMA and Long str...	1	1	NaN	Yes	0	21	12000000.0	1.082878e+07	0.0	-0.097602	Completed
1	TA - SMA for: ^DJI	1	1	1.0	No	0	6	2000000.0	1.871721e+06	0.0	-0.064139	Completed
2	TA - SMA for: ^IXIC	2	2	1.0	No	0	8	2000000.0	1.720096e+06	0.0	-0.139952	Completed
3	TA - SMA for: ^GSPC	3	3	1.0	No	0	4	2000000.0	1.862138e+06	0.0	-0.068931	Completed
4	Long-Only Trade Strategy for ^GSPC	4	4	1.0	No	0	1	2000000.0	1.789374e+06	0.0	-0.105313	Completed
5	Long-Only Trade Strategy for ^IXIC	5	5	1.0	No	0	1	2000000.0	1.702247e+06	0.0	-0.148877	Completed
6	Long-Only Trade Strategy for ^DJI	6	6	1.0	No	0	1	2000000.0	1.883204e+06	0.0	-0.058398	Completed

Portfolio B

Portfolio B: Long and AI LSTM based Strategies for US Indixes

Description:

1. Portfolio with 6 trading strategies
2. $1.2M Budget
3. No-Rebalance during the backtest 
4. 100% capital invested
5. Portfolio investment using equal weights for each trade strategy

Trade Strategies:

1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold
2. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using LSTM AI Model 

Backtest:

1. Type: regular
2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2005-12-31  
    2. Validation Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2007-06-30   
4. Test Period: 
    1. Begin Date = 2008-01-01 
    2. End Date   = 2008-12-31 

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 14:00:27,003 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

ts_model_pipline_mng_b = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_B",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Found module already imported: WaveNet_RNN_AI_PipelineBuilder

ts_model_pipline_mng_b.getAllModelDataPipelineDataForStockSymbol(stockSymbol = "^GSPC" )

	Adj Close	Close	High	Low	Open	Volume	Index
Date
2000-01-03	0.201711	0.201711	0.203276	0.199999	0.204862	0.051867	^GSPC
2000-01-04	0.187257	0.187257	0.197360	0.189389	0.201224	0.058822	^GSPC
2000-01-05	0.187954	0.187954	0.186465	0.184270	0.186753	0.065713	^GSPC
2000-01-06	0.188301	0.188301	0.186110	0.188008	0.187451	0.066326	^GSPC
2000-01-07	0.198149	0.198149	0.193789	0.190245	0.187799	0.078299	^GSPC
...	...	...	...	...	...	...	...
2021-09-14	0.975676	0.975676	0.984374	0.976878	0.985465	0.199336	^GSPC
2021-09-15	0.985429	0.985429	0.984683	0.977633	0.977208	0.252131	^GSPC
2021-09-16	0.983629	0.983629	0.984423	0.979040	0.984884	0.267110	^GSPC
2021-09-17	0.973070	0.973070	0.980696	0.974882	0.982978	0.474420	^GSPC
2021-09-20	0.953575	0.953575	0.962889	0.943298	0.965657	0.201996	^GSPC

5464 rows × 7 columns

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

# Conducting a backtest for a Trade Strategy requires:
#
#    1. A Trade Strategy Model 
#         - A TS Model is associated with only on trade asset 
#         - References a Market Data Manager 
#         - Trading Rules: Entry, Exit,Stop-Loss, Trail-Stop-Loss, etc.  (only Entry rules are required)
#         - Each Trading Action may have a Pipeline Data Manager associated with. 
#           - For example: For using SMA to enter/exit trades, the signasl must be computed based on the
#                        Market Data and SMA indicators/signals configured
#           - Different trade actions might share the same pipeline manager or create different onces
#         - Each Trading Action has their own configuration/metadata
#           - Entry Rules: 
#               Decide how to combine signals and which signals to enter/exit
#               Entry Policies:  
#                  - Only enter a trade if the last n dates/periods were marked for entering a trade
#                  - Only enter a trade if there is a cross-over signal (from buy to sell, or sell to buy)
#               More complex configuration options are availabe for creating entry rules based on an ensemble
#               of entry rule algorithms. This will be explained and detail in other examples.
#           
#    2. A Broker to conduct the trades with 
#         - can be shared across Trade Strategies, but no required
#
#    3. A Trade Strategy Investment Manager
#         - Manages the budget and spending rules (max/min to invest)
#         - Trades 
#         - Interfaces with Broker objects to place, cancel, alter trades
#         - Other investment metadata rules
#         - Works with Portfolio Investment Managers to rebalance funds across a portfolio
#
#    4. A Trade Asset Register Item
#         - This is a compose object to have all necessary information to conduct a backtest 
#           either as a single item, or as part of a portfolio
#         - The dates and characteristics of the backtest execution are defined separately


# Each Trade Register Item Object has:
#   - A Name 
#   - One Trade Strategy
#       + Each Trade Strategy has one or more actions defined (only entry rules are mandatory)
#           a) Entry Rules
#           b) Exit Rules
#           c) Stop Loss and/or Trail Stop Rules
#           d) Position Sizing Rules per trade 
#           etc.
#   - One Investment Manager
#   - A broker to trade with (simulation, or paper trading)

taRegisterItems = [ ]

for trade_asset in portfolio_trade_assets :
    
    # LSTM Trading Model, Configure with Trading Rules 
    #
    # There are two parts two configured an AI Trading Model:
    #
    #    1) Defined the Actual AI Architecture and configured it:
    #         - for this example it is an LSTM model with two layers, the model params are customed
    #         - This LSTM network uses two x-features for predictions, and predicts 5 steps ahead
    #         - AI Models are derived from the class : AI_ModelBase 
    #
    #.   2) Defined the Trading Rules for an AI Model
    #         - How predictions from an AI model translated to invest/no-invest signals, and 
    #           trade type (long or short)
    #         - AI Models are derived from the class : AI_TradingModelBase 
    #         - The WaveNet_RNN_AI_TradingModel class has a generic set of trading rules for DL
    #           time series forecasting models
    #         - The Trading Model feeds the Pipeline data built for the AI model in tensor format
    #
    #         - Configured the trading model metadata
    #                - x-features : 'Adj Close', 'Volume'  { for predicting , could be more or less}
    #                - y-label    : 'Adj Close'            { just predict adj close }
    #                - creates time series that are 50 steps long for predicting
    #                - The AI time series model will predict 5 steps ahead 
    #            
    #         - The following trading metadata rules are defined with the following params:           
    #              a) trad_pred_rule = 
    #                     i)  WaveNet_RNN_AI_Trading_Rules.n_pred_ahead  
    #                            { use n days ahead prediction } or
    #                     ii) WaveNet_RNN_AI_Trading_Rules.n_pred_mean  
    #                            { predict using the mean of n days ahead }        
    #               b) trad_pred_dat  = [ n ]   { predict using n periods ahead, 0 = Next Period }
    #               c) extend_trade_exit_dyn    = [ True, False ]
    #                     Yes, an open trade will be extended based on current forecast of data
    #                     ex: If a trade was opened in a long position with 5 days ahead prediction, 
    #                         if option is enable, every period, the trade will be analyzed for extending  
    #               d) re_evaluate_forecast_exit_dyn  = [ True, False ]  
    #                      True = exit trade earlier than first forecast, base on new data
    #                      ex: if a trade was opened in a long position using 5 days ahead forecast,
    #                          if this option is enable, the trade can be exited after any period,
    #                          if the LSTM Model starts predicting a different signal/trade-type than the
    #                          one entered.
    #

    num_x_features = 2 
    num_y_predict_ahead_periods = 5
    
    ai_model = LSTM_Model( epochs = 20, 
                           num_x_features         = num_x_features,
                           num_y_predictions      = num_y_predict_ahead_periods,
                           lstm_layers_neuros     = [ 20, 20 ],  # number of LSTM stack layers, and thier num of neurons
                           optimizer              = "adam",
                           activation             = "relu",
                           rand_seed              = 42 ) 
     
    ai_trading_model   = WaveNet_RNN_AI_TradingModel(\
                               name                           = "LSTM_{0}".format(trade_asset),
                               ai_model                       = ai_model,
                               num_x_features                 = num_x_features,
                               features                       = ['Adj Close', 'Volume'], 
                               features_label                 = 'Adj Close',
                               n_steps                        = 50, 
                               n_pred_ahead                   = num_y_predict_ahead_periods,
                               trad_pred_rule                 = WaveNet_RNN_AI_Trading_Rules.n_pred_ahead,
                               trad_pred_dat                  = [ 0 ], 
                               extend_trade_exit_dyn          = True,
                               re_evaluate_forecast_exit_dyn  = True )
    # *** TS Entry Rules 
    # *** Uses AI Trading Model configured above to determine when to enter a trade
    
    ts_enter_ai = TradeEnterAIStrategy(name = "Enter_LSTM",
                                       ai_trading_model = ai_trading_model, 
                                       ts_model_pipline_mng = ts_model_pipline_mng_b ) 
                                                     
    ts_enter_ai_set = TradeEnterStack( tsEntries = [ ts_enter_ai ],
                                       tsPolicy  = TradeEnterSetPolicy( \
                                                        lastn_match = 1,
                                                        votingPolicy = TradeEnterSetVotingPolicy.Majority ) 
                                     ) 

    # *** TS Exit Rules 
    # *** Uses AI Trading Model configured above to determine when to enter a trade
     
    ts_exit_ai = TradeExitAIStrategy(name = "Exit_LSTM",
                                     ai_trading_model = ai_trading_model,
                                     ts_model_pipline_mng = ts_model_pipline_mng_b ) 
                                                     
    ts_exit_ai_set = TradeExitStack( tsExits = [ ts_exit_ai] )
                                                      
    # *** Trade Strategy 
        
    ts_model = TradeStrategyBaseModelPersist(\
                    trade_asset               = trade_asset,
                    ts_marketdat_mng          = ts_marketdat_mng,
                    ts_enter                  = ts_enter_ai_set,
                    ts_exit                   = ts_exit_ai_set,                       
                    model_type                = "lstm",
                    model_version             = "1.0",
                    market_data_period        = MarketDataPeriod.Day,
                ) 
    
    # *** Trade Strategy Investment Manager 
    
    taInvMng = TradingAssetInvMngBase(\
                                    trade_asset                = trade_asset,
                                    minPeriodsBetweenTrades    = 1,
                                    initInvestment             = 1000000.0, 
                                    capPercentToInvestPerTrade = 100.0,
                                    minInvestPercent           = 5.0,
                                    min_account_balance        = 1000.0 )

    # *** Trade Register Item
    # *** This object has all configuration defined to execute a given trade strategy,
    # *** It can be executed individually in backtest or as part of a portfolio
    
    ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "TA - LSTM for: {0}".format(trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  
 
    taRegisterItems.append(ta_registered_item)
    

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - LSTM Algo",
                  initInvestment             = 12000000.0, 
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                            name                 = "Portfolio B, US-Indexes using LSTM and Long strategies",
                            taRegisterItems      = taRegisterItems,
                            portfolioInvMngObj   = portfInvMng,
                            tradingExecutionType = TradingAssetExecutionType.Simulation,
                            add_long_only_ts     = True )

2021-10-28 14:05:10,530 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio B, US-Indexes using LSTM and Long strategies" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

2021-10-28 14:05:21,930 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 14:05:21,930 [INFO] root: PortfolioRegisterItem init called. 
Portfolio of US-Indexes using SMA and Long strategies
Portfolio of US-Indexes using LSTM and Long strategies
2 Portfolio of US-Indexes using LSTM and Long strategies

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2000-07-01",
                             strBegDate         = "2001-01-01",
                             strEndDate         = "2005-12-30" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-06-30" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2008-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for LSTM based Trade Strategies",
                    timePeriod  = testing_period,
                    fitDatasets = fit_datasets,
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()

	TA_Reg_Name	TA_Reg_Id	Inv_Mng_Id	Inv_Mng_Parent_Id	Is_Portfolio	Num_Trades_Executing	Num_Trades_Completed	Init_Capital	Final_Capital	Final_Inv_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes using LSTM and Long st...	2	2	NaN	Yes	0	103	12000000.0	1.366645e+07	0.0	0.138871	Completed
1	TA - LSTM for: ^DJI	7	7	2.0	No	0	45	2000000.0	3.635315e+06	0.0	0.817657	Completed
2	TA - LSTM for: ^IXIC	8	8	2.0	No	0	50	2000000.0	4.760641e+06	0.0	1.380321	Completed
3	TA - LSTM for: ^GSPC	9	9	2.0	No	0	5	2000000.0	1.466085e+06	0.0	-0.266957	Completed
4	Long-Only Trade Strategy for ^GSPC	10	10	2.0	No	0	1	2000000.0	1.248316e+06	0.0	-0.375842	Completed
5	Long-Only Trade Strategy for ^IXIC	11	11	2.0	No	0	1	2000000.0	1.209029e+06	0.0	-0.395486	Completed
6	Long-Only Trade Strategy for ^DJI	12	12	2.0	No	0	1	2000000.0	1.347062e+06	0.0	-0.326469	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio of US-Indexes using LSTM and Long st...	Yes			2008-01-03	2008-12-31	1.0	0.14	0.14	0.57	-0.23	0.11	-0.07
1	TA - LSTM for: ^DJI	No	lstm	^DJI	2008-01-03	2008-12-31	1.0	0.82	0.82	1.76	-0.22	0.11	-0.08
2	TA - LSTM for: ^IXIC	No	lstm	^IXIC	2008-01-03	2008-12-31	1.0	1.38	1.39	2.31	-0.21	0.12	-0.07
3	TA - LSTM for: ^GSPC	No	lstm	^GSPC	2008-01-03	2008-12-31	1.0	-0.27	-0.27	-0.55	-0.48	0.12	-0.09
4	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2008-01-03	2008-12-31	1.0	-0.38	-0.38	-0.94	-0.48	0.12	-0.09
5	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2008-12-31	1.0	-0.4	-0.4	-1.02	-0.49	0.12	-0.09
6	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2008-12-31	1.0	-0.33	-0.33	-0.86	-0.42	0.11	-0.08

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0	1	2	3	4	5	6
ta_reg_item_name	Portfolio of US-Indexes using LSTM and Long st...	TA - LSTM for: ^DJI	TA - LSTM for: ^IXIC	TA - LSTM for: ^GSPC	Long-Only Trade Strategy for ^GSPC	Long-Only Trade Strategy for ^IXIC	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	2	7	8	9	10	11	12
is_portfolio	Yes	No	No	No	No	No	No
model_type		lstm	lstm	lstm	long	long	long
trade_asset		^DJI	^IXIC	^GSPC	^GSPC	^IXIC	^DJI
test_period_len_days	365	365	365	365	365	365	365
tr_period_len_days	1824	1824	1824	1824	1824	1824	1824
val_period_len_days	545	545	545	545	545	545	545
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03
End Period	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	1.0	1.0	1.0	1.0
Cumulative Return	0.14	0.82	1.38	-0.27	-0.38	-0.4	-0.33
CAGR%	0.14	0.82	1.39	-0.27	-0.38	-0.4	-0.33
Sharpe	0.57	1.76	2.31	-0.55	-0.94	-1.02	-0.86
Sortino	0.93	2.98	4.08	-0.79	-1.29	-1.4	-1.2
Sortino/√2	0.66	2.11	2.88	-0.56	-0.91	-0.99	-0.85
Max Drawdown	-0.23	-0.22	-0.21	-0.48	-0.48	-0.49	-0.42
Longest DD Days	118	63	54	362	362	362	240
Volatility (ann.)	0.31	0.38	0.41	0.41	0.41	0.41	0.38
Calmar	0.62	3.73	6.52	-0.56	-0.79	-0.8	-0.78
Skew	1.3	0.75	0.81	0.45	0.18	0.21	0.43
Kurtosis	8.97	3.92	2.76	4.01	3.99	3.22	4.22
Expected Daily %	0.0	0.0	0.0	-0.0	-0.0	-0.0	-0.0
Expected Monthly %	0.01	0.05	0.07	-0.03	-0.04	-0.04	-0.03
Expected Yearly %	0.14	0.82	1.38	-0.27	-0.38	-0.4	-0.33
Kelly Criterion	0.06	0.15	0.19	-0.06	-0.1	-0.09	-0.08
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.03	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Expected Shortfall (cVaR)	-0.03	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Gain/Pain Ratio	0.14	0.39	0.52	-0.1	-0.16	-0.17	-0.15
Gain/Pain (1M)	0.87	4.82	13.96	-0.46	-0.81	-0.69	-0.82
Payoff Ratio	1.19	1.22	1.23	0.88	0.83	0.96	0.92
Profit Factor	1.14	1.39	1.52	0.9	0.84	0.83	0.85
Common Sense Ratio	1.52	1.81	2.02	0.89	0.76	0.77	0.74
CPC Index	0.66	0.9	1.03	0.4	0.35	0.37	0.38
Tail Ratio	1.34	1.31	1.33	0.99	0.91	0.92	0.86
Outlier Win Ratio	5.22	4.35	4.62	4.92	4.26	3.61	4.23
Outlier Loss Ratio	5.51	3.65	3.27	3.85	4.3	4.04	4.23
MTD	0.2	0.3	0.31	0.21	0.01	0.03	-0.01
3M	0.08	0.19	0.39	-0.04	-0.18	-0.2	-0.15
6M	0.18	0.81	0.86	-0.17	-0.29	-0.31	-0.23
YTD	0.14	0.82	1.38	-0.27	-0.38	-0.4	-0.33
1Y	0.14	0.82	1.38	-0.27	-0.38	-0.4	-0.33
3Y (ann.)	0.14	0.82	1.39	-0.27	-0.38	-0.4	-0.33
5Y (ann.)	0.14	0.82	1.39	-0.27	-0.38	-0.4	-0.33
10Y (ann.)	0.14	0.82	1.39	-0.27	-0.38	-0.4	-0.33
All-time (ann.)	0.14	0.82	1.39	-0.27	-0.38	-0.4	-0.33
Best Day	0.11	0.11	0.12	0.12	0.12	0.12	0.11
Worst Day	-0.07	-0.08	-0.07	-0.09	-0.09	-0.09	-0.08
Best Month	0.2	0.3	0.31	0.21	0.05	0.06	0.05
Worst Month	-0.08	-0.1	-0.06	-0.17	-0.17	-0.18	-0.14
Best Year	0.14	0.82	1.38	-0.27	-0.38	-0.4	-0.33
Worst Year	0.14	0.82	1.38	-0.27	-0.38	-0.4	-0.33
Avg. Drawdown	-0.04	-0.04	-0.04	-0.48	-0.48	-0.49	-0.26
Avg. Drawdown Days	28	18	9	362	362	362	180
Recovery Factor	0.61	3.7	6.46	-0.56	-0.78	-0.8	-0.78
Ulcer Index	3.4	1.8	1.46	inf	inf	1.56	1.86
Avg. Up Month	0.06	0.11	0.12	0.07	0.02	0.03	0.02

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Re-Store Backtest

session.expunge_all()
del tsExecuteEngine

for tsObj in session.query(BacktestExecution):  
    print(tsObj.name)
    if  tsObj.name  == "Backtest for LSTM based Trade Strategies" :
        break

tsExecuteEngine = tsObj
print(tsExecuteEngine.id, tsExecuteEngine.name)

2021-10-28 14:10:27,670 [INFO] root: BacktestExecution init called. 
2021-10-28 14:10:27,676 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 14:10:27,677 [INFO] root: Loaded TradingAssetRegisterBase object
2021-10-28 14:10:27,683 [INFO] root: BacktestExecutionDatasets init called. 
2021-10-28 14:10:27,684 [INFO] root: BacktestExecution init called. 
2021-10-28 14:10:27,690 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 14:10:27,691 [INFO] root: Loaded TradingAssetRegisterBase object
2021-10-28 14:10:27,697 [INFO] root: BacktestExecutionDatasets init called. 
Backtest for SMA based Trade Strategies
Backtest for LSTM based Trade Strategies
2 Backtest for LSTM based Trade Strategies

tsExecuteEngine.getExecutionMetrics()

2021-10-28 14:10:31,759 [INFO] root: PortfolioInvMng init called. 
2021-10-28 14:10:31,760 [INFO] root: PortfolioInvMng init called. 
2021-10-28 14:10:31,777 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-28 14:10:31,778 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-28 14:10:31,779 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-28 14:10:31,780 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-28 14:10:31,780 [INFO] root: TradingAssetRegisterItem init called. 
2021-10-28 14:10:31,781 [INFO] root: TradingAssetRegisterItem init called. 
**** TradingAssetInvMngBase init called. 
2021-10-28 14:10:31,787 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-28 14:10:31,809 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-28 14:10:31,830 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-28 14:10:31,848 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-28 14:10:31,865 [INFO] root: TradingAssetInvMngBase init called. 
**** TradingAssetInvMngBase init called. 
2021-10-28 14:10:31,882 [INFO] root: TradingAssetInvMngBase init called. 

/Users/gustavozambrana/venvs/CFRM505/lib/python3.7/site-packages/sqlalchemy/orm/loading.py:628: SAWarning: Loading context for <PortfolioInvMng at 0x7f9c46745910> has changed within a load/refresh handler, suggesting a row refresh operation took place. If this event handler is expected to be emitting row refresh operations within an existing load or refresh operation, set restore_load_context=True when establishing the listener to ensure the context remains unchanged when the event handler completes.
  "handler completes." % (state_str(state),)

	TA_Reg_Name	TA_Reg_Id	Inv_Mng_Id	Inv_Mng_Parent_Id	Is_Portfolio	Num_Trades_Executing	Num_Trades_Completed	Init_Capital	Final_Capital	Final_Inv_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes using LSTM and Long st...	2	2	NaN	Yes	0	103	12000000.0	1.366645e+07	0.0	0.138871	Completed
1	TA - LSTM for: ^DJI	7	7	2.0	No	0	45	2000000.0	3.635315e+06	0.0	0.817657	Completed
2	TA - LSTM for: ^IXIC	8	8	2.0	No	0	50	2000000.0	4.760641e+06	0.0	1.380321	Completed
3	TA - LSTM for: ^GSPC	9	9	2.0	No	0	5	2000000.0	1.466085e+06	0.0	-0.266957	Completed
4	Long-Only Trade Strategy for ^GSPC	10	10	2.0	No	0	1	2000000.0	1.248316e+06	0.0	-0.375842	Completed
5	Long-Only Trade Strategy for ^IXIC	11	11	2.0	No	0	1	2000000.0	1.209029e+06	0.0	-0.395486	Completed
6	Long-Only Trade Strategy for ^DJI	12	12	2.0	No	0	1	2000000.0	1.347062e+06	0.0	-0.326469	Completed

Portfolio C

1. Portfolio C: Long and Multiple Configured SMA Trade Strategies for US Indixes

   Description:

    1. Portfolio with 27 trading strategies
    2. $200,000/strategy, Total Budget = $5.4M
    3. No-Rebalance during the backtest 
    4. 100% capital invested
    5. Portfolio investment using equal weights for each trade strategy
   
   

   Trade Strategies:

    1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold
    2. 21 trade strategies: Nasdaq, S&P 500, and DOW Jones using following SMA Configuration:
   
       sma-conf fast_mav, slow_mav, enter_if_last_nmatch, enter_if_cross_over_sig, exit_if_last_nmatch
            1       20       100          2                    True                     2
            2       10        50          2                    True                     2
            3       10        50          2                    True                     1
            4       15        60          2                    True                     2
            5       15        75          2                    True                     1
            6        7        35          2                    True                     2
            7        7        35          2                    True                     1               
   
    3. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using the following configuration: 
   
        - A Composite SMA Trade Strategy based on the the first five SMA Configured trade strategies
          above
   
        - The Set Entry cofiguration rules are: 
          a. Majority Voting (default)
             Three of the Five SMA Indicators configured in the set must signaled the same type of
             investment decision.
          b. Enter a trade if the last n voting decisions are the same, set to 1 (default )
          c. Enter a trade if there is cross-over signaled after applying rules a) and b), 
             set to False (default)
   
        - The Set Exit configuration rules are: 
          a. Majority Voting (Default)
              - Exit a long trade if the percent of SMA Indicators signaling an exit for a long position
                 is greater that the percent signaling no-exit 
              - Exit a short trade if the percent of SMA Indicators signaling an exit for a short position
                 is greater that the percent signaling no-exit 
          b. Exit a trade if the last n exit decisions are the same, set to 1 (default )
   
    4. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using the following configuration: 
   
        - A Composite SMA Trade Strategy based on all the SMA Configured trade strategies
   
        - The Set Entry cofiguration rules are: 
          a. Voting Percent
             The majority of the signals indicating an investment position (either short or long), 
             must also meet a minimum percent of 50% across all the strategies in the set.
          b. Enter a trade if the last n voting decisions are the same, set to 1 (default )
          c. Enter a trade if there is cross-over signaled after applying rules a) and b), 
             set to False (default)
   
        - The Set Exit configuration rules are: 
          a. Majority Voting (Default)
              - Exit a long trade if the percent of SMA Indicators signaling an exit for a long position
                 is greater that the percent signaling no-exit 
              - Exit a short trade if the percent of SMA Indicators signaling an exit for a short position
                 is greater that the percent signaling no-exit 
          b. Exit a trade if the last n exit decisions are the same, set to 1 (default )
   
   

   Backtest:

    1. Type: regular
    2. Test Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2001-12-31 

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 15:21:44,809 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_1",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 20, 'win_slow' : 100 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_3",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_4",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_5",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 75 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #6

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_6",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 7, 'win_slow' : 35 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #7

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_7",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 7, 'win_slow' : 35 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = None )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = None )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  24

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Multiple-SMA TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio C, US-Indexes with Multiple-SMA and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 15:22:00,268 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio C, US-Indexes with Multiple-SMA and Long strategies" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

2021-10-28 15:20:44,078 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 15:20:44,078 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 15:20:44,079 [INFO] root: PortfolioRegisterItem init called. 
Portfolio of US-Indexes using SMA and Long strategies
1 Portfolio of US-Indexes using SMA and Long strategies

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio of Multiple-SMA based Trade Strategies",
                    timePeriod = DatasetDef(datasetType = DatasetType.Testing,
                                            strBegDate = "2001-01-01", 
                                            strEndDate = "2001-12-31" ),
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']]

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes with Multiple-SMA and ...	Yes	158	54000000.0	5.772265e+07	0.068938	Completed
1	ts_sma_1_^DJI	No	6	2000000.0	1.918609e+06	-0.040696	Completed
2	ts_sma_2_^DJI	No	7	2000000.0	2.170411e+06	0.085205	Completed
3	ts_sma_3_^DJI	No	7	2000000.0	2.218299e+06	0.109149	Completed
4	ts_sma_4_^DJI	No	7	2000000.0	1.842030e+06	-0.078985	Completed
5	ts_sma_5_^DJI	No	5	2000000.0	1.895885e+06	-0.052058	Completed
6	ts_sma_6_^DJI	No	7	2000000.0	2.156208e+06	0.078104	Completed
7	ts_sma_7_^DJI	No	7	2000000.0	2.227205e+06	0.113602	Completed
8	all_^DJI	No	7	2000000.0	2.187996e+06	0.093998	Completed
9	ts_sma_1_^IXIC	No	8	2000000.0	2.140912e+06	0.070456	Completed
10	ts_sma_2_^IXIC	No	6	2000000.0	2.443404e+06	0.221702	Completed
11	ts_sma_3_^IXIC	No	6	2000000.0	2.401716e+06	0.200858	Completed
12	ts_sma_4_^IXIC	No	6	2000000.0	2.551934e+06	0.275967	Completed
13	ts_sma_5_^IXIC	No	4	2000000.0	2.126019e+06	0.063009	Completed
14	ts_sma_6_^IXIC	No	8	2000000.0	2.202711e+06	0.101356	Completed
15	ts_sma_7_^IXIC	No	8	2000000.0	2.168078e+06	0.084039	Completed
16	all_^IXIC	No	6	2000000.0	2.336705e+06	0.168352	Completed
17	ts_sma_1_^GSPC	No	4	2000000.0	2.023192e+06	0.011596	Completed
18	ts_sma_2_^GSPC	No	6	2000000.0	2.208141e+06	0.104071	Completed
19	ts_sma_3_^GSPC	No	6	2000000.0	2.256189e+06	0.128095	Completed
20	ts_sma_4_^GSPC	No	6	2000000.0	1.903230e+06	-0.048385	Completed
21	ts_sma_5_^GSPC	No	6	2000000.0	2.048397e+06	0.024198	Completed
22	ts_sma_6_^GSPC	No	8	2000000.0	2.331342e+06	0.165671	Completed
23	ts_sma_7_^GSPC	No	8	2000000.0	2.334666e+06	0.167333	Completed
24	all_^GSPC	No	6	2000000.0	2.254545e+06	0.127272	Completed
25	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	1.789374e+06	-0.105313	Completed
26	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	1.702247e+06	-0.148877	Completed
27	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	1.883204e+06	-0.058398	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio of US-Indexes with Multiple-SMA and ...	Yes			2001-01-03	2001-12-31	1.0	0.07	0.07	0.52	-0.12	0.04	-0.03
1	ts_sma_1_^DJI	No	ts_sma_1	^DJI	2001-01-03	2001-12-31	1.0	-0.04	-0.04	-0.13	-0.19	0.06	-0.04
2	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2001-01-03	2001-12-31	1.0	0.09	0.09	0.54	-0.12	0.06	-0.04
3	ts_sma_3_^DJI	No	ts_sma_3	^DJI	2001-01-03	2001-12-31	0.97	0.11	0.11	0.66	-0.11	0.06	-0.04
4	ts_sma_4_^DJI	No	ts_sma_4	^DJI	2001-01-03	2001-12-31	1.0	-0.08	-0.08	-0.35	-0.15	0.06	-0.04
5	ts_sma_5_^DJI	No	ts_sma_5	^DJI	2001-01-03	2001-12-31	0.98	-0.05	-0.05	-0.19	-0.19	0.06	-0.04
6	ts_sma_6_^DJI	No	ts_sma_6	^DJI	2001-01-03	2001-12-31	1.0	0.08	0.08	0.5	-0.14	0.06	-0.04
7	ts_sma_7_^DJI	No	ts_sma_7	^DJI	2001-01-03	2001-12-31	0.97	0.11	0.11	0.67	-0.13	0.06	-0.04
8	all_^DJI	No	all	^DJI	2001-01-03	2001-12-31	0.99	0.09	0.09	0.58	-0.12	0.06	-0.04
9	ts_sma_1_^IXIC	No	ts_sma_1	^IXIC	2001-01-03	2001-12-31	1.0	0.07	0.07	0.38	-0.28	0.06	-0.05
10	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2001-01-03	2001-12-31	1.0	0.22	0.22	0.78	-0.25	0.06	-0.05
11	ts_sma_3_^IXIC	No	ts_sma_3	^IXIC	2001-01-03	2001-12-31	0.98	0.2	0.2	0.73	-0.26	0.06	-0.05
12	ts_sma_4_^IXIC	No	ts_sma_4	^IXIC	2001-01-03	2001-12-31	1.0	0.28	0.28	0.93	-0.23	0.06	-0.05
13	ts_sma_5_^IXIC	No	ts_sma_5	^IXIC	2001-01-03	2001-12-31	0.66	0.06	0.06	0.36	-0.26	0.06	-0.05
14	ts_sma_6_^IXIC	No	ts_sma_6	^IXIC	2001-01-03	2001-12-31	1.0	0.1	0.1	0.46	-0.3	0.06	-0.05
15	ts_sma_7_^IXIC	No	ts_sma_7	^IXIC	2001-01-03	2001-12-31	0.97	0.08	0.08	0.41	-0.29	0.06	-0.05
16	all_^IXIC	No	all	^IXIC	2001-01-03	2001-12-31	1.0	0.17	0.17	0.64	-0.28	0.06	-0.05
17	ts_sma_1_^GSPC	No	ts_sma_1	^GSPC	2001-01-03	2001-12-31	1.0	0.01	0.01	0.15	-0.22	0.04	-0.03
18	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2001-01-03	2001-12-31	1.0	0.1	0.1	0.65	-0.11	0.04	-0.03
19	ts_sma_3_^GSPC	No	ts_sma_3	^GSPC	2001-01-03	2001-12-31	0.98	0.13	0.13	0.77	-0.11	0.04	-0.03
20	ts_sma_4_^GSPC	No	ts_sma_4	^GSPC	2001-01-03	2001-12-31	1.0	-0.05	-0.05	-0.18	-0.16	0.04	-0.04
21	ts_sma_5_^GSPC	No	ts_sma_5	^GSPC	2001-01-03	2001-12-31	0.98	0.02	0.02	0.22	-0.15	0.04	-0.03
22	ts_sma_6_^GSPC	No	ts_sma_6	^GSPC	2001-01-03	2001-12-31	1.0	0.17	0.17	0.93	-0.14	0.04	-0.03
23	ts_sma_7_^GSPC	No	ts_sma_7	^GSPC	2001-01-03	2001-12-31	0.95	0.17	0.17	0.95	-0.12	0.04	-0.03
24	all_^GSPC	No	all	^GSPC	2001-01-03	2001-12-31	1.0	0.13	0.13	0.77	-0.11	0.04	-0.03
25	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2001-01-03	2001-12-31	1.0	-0.11	-0.11	-0.42	-0.3	0.05	-0.05
26	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2001-01-03	2001-12-31	1.0	-0.15	-0.15	-0.17	-0.5	0.14	-0.07
27	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2001-01-03	2001-12-31	1.0	-0.06	-0.06	-0.18	-0.27	0.04	-0.07

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0	1	2	3	4	5	6	7	8	9	...	18	19	20	21	22	23	24	25	26	27
ta_reg_item_name	Portfolio of US-Indexes with Multiple-SMA and ...	ts_sma_1_^DJI	ts_sma_2_^DJI	ts_sma_3_^DJI	ts_sma_4_^DJI	ts_sma_5_^DJI	ts_sma_6_^DJI	ts_sma_7_^DJI	all_^DJI	ts_sma_1_^IXIC	...	ts_sma_2_^GSPC	ts_sma_3_^GSPC	ts_sma_4_^GSPC	ts_sma_5_^GSPC	ts_sma_6_^GSPC	ts_sma_7_^GSPC	all_^GSPC	Long-Only Trade Strategy for ^GSPC	Long-Only Trade Strategy for ^IXIC	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	None	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
is_portfolio	Yes	No	No	No	No	No	No	No	No	No	...	No	No	No	No	No	No	No	No	No	No
model_type		ts_sma_1	ts_sma_2	ts_sma_3	ts_sma_4	ts_sma_5	ts_sma_6	ts_sma_7	all	ts_sma_1	...	ts_sma_2	ts_sma_3	ts_sma_4	ts_sma_5	ts_sma_6	ts_sma_7	all	long	long	long
trade_asset		^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^IXIC	...	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^IXIC	^DJI
test_period_len_days	364	364	364	364	364	364	364	364	364	364	...	364	364	364	364	364	364	364	364	364	364
tr_period_len_days	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
val_period_len_days	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	...	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	...	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03	2001-01-03
End Period	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	...	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31	2001-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	0.97	1.0	0.98	1.0	0.97	0.99	1.0	...	1.0	0.98	1.0	0.98	1.0	0.95	1.0	1.0	1.0	1.0
Cumulative Return	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
CAGR%	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
Sharpe	0.52	-0.13	0.54	0.66	-0.35	-0.19	0.5	0.67	0.58	0.38	...	0.65	0.77	-0.18	0.22	0.93	0.95	0.77	-0.42	-0.17	-0.18
Sortino	0.76	-0.19	0.84	1.04	-0.52	-0.29	0.76	1.04	0.91	0.56	...	0.98	1.18	-0.26	0.34	1.43	1.47	1.18	-0.59	-0.25	-0.25
Sortino/√2	0.53	-0.13	0.59	0.74	-0.37	-0.21	0.54	0.73	0.64	0.4	...	0.69	0.84	-0.19	0.24	1.01	1.04	0.83	-0.42	-0.18	-0.18
Max Drawdown	-0.12	-0.19	-0.12	-0.11	-0.15	-0.19	-0.14	-0.13	-0.12	-0.28	...	-0.11	-0.11	-0.16	-0.15	-0.14	-0.12	-0.11	-0.3	-0.5	-0.27
Longest DD Days	167	181	160	168	259	283	256	178	167	270	...	155	155	254	168	167	165	155	334	340	223
Volatility (ann.)	0.16	0.19	0.19	0.19	0.19	0.19	0.19	0.19	0.19	0.31	...	0.18	0.18	0.19	0.18	0.19	0.18	0.18	0.21	0.43	0.21
Calmar	0.57	-0.22	0.69	0.98	-0.52	-0.27	0.56	0.91	0.81	0.25	...	0.98	1.17	-0.3	0.16	1.24	1.4	1.21	-0.36	-0.3	-0.22
Skew	0.11	0.35	0.59	0.61	0.73	0.72	0.53	0.51	0.59	0.16	...	0.26	0.27	0.33	0.35	0.24	0.26	0.28	0.1	0.65	-0.47
Kurtosis	1.3	2.84	2.61	2.78	2.93	3.14	2.5	2.56	2.62	-0.07	...	0.56	0.65	0.79	0.65	0.48	0.65	0.51	1.59	2.92	3.72
Expected Daily %	0.0	-0.0	0.0	0.0	-0.0	-0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	-0.0	0.0	0.0	0.0	0.0	-0.0	-0.0	-0.0
Expected Monthly %	0.01	-0.0	0.01	0.01	-0.01	-0.0	0.01	0.01	0.01	0.01	...	0.01	0.01	-0.0	0.0	0.01	0.01	0.01	-0.01	-0.01	-0.01
Expected Yearly %	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
Kelly Criterion	0.04	-0.01	0.04	0.05	-0.03	-0.02	0.04	0.05	0.05	0.03	...	0.05	0.06	-0.01	0.02	0.07	0.08	0.06	-0.04	-0.01	-0.02
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.03	...	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.04	-0.02
Expected Shortfall (cVaR)	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.03	...	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.04	-0.02
Gain/Pain Ratio	0.09	-0.02	0.09	0.12	-0.06	-0.03	0.09	0.12	0.1	0.06	...	0.11	0.13	-0.03	0.04	0.16	0.17	0.13	-0.07	-0.03	-0.03
Gain/Pain (1M)	0.56	-0.1	0.68	0.99	-0.21	-0.12	0.4	0.62	0.78	0.24	...	1.12	1.36	-0.13	0.19	1.21	1.33	1.34	-0.26	-0.1	-0.13
Payoff Ratio	1.0	0.96	1.07	1.09	1.14	1.08	1.1	1.07	1.07	1.08	...	1.04	1.05	1.04	1.08	1.04	1.02	1.06	1.0	0.95	0.97
Profit Factor	1.09	0.98	1.09	1.12	0.94	0.97	1.09	1.12	1.1	1.06	...	1.11	1.13	0.97	1.04	1.16	1.17	1.13	0.93	0.97	0.97
Common Sense Ratio	1.13	1.15	1.43	1.5	1.18	1.13	1.35	1.39	1.44	1.1	...	1.27	1.38	1.08	1.21	1.33	1.36	1.38	0.93	0.94	0.96
CPC Index	0.57	0.47	0.59	0.62	0.49	0.49	0.59	0.61	0.6	0.57	...	0.6	0.62	0.49	0.55	0.64	0.64	0.62	0.45	0.47	0.47
Tail Ratio	1.03	1.18	1.31	1.34	1.25	1.17	1.24	1.24	1.3	1.03	...	1.15	1.22	1.11	1.17	1.14	1.16	1.22	1.0	0.97	0.99
Outlier Win Ratio	3.76	3.54	3.39	3.52	3.28	3.51	3.29	3.45	3.41	2.99	...	3.06	3.17	3.12	3.07	2.97	3.24	3.02	3.82	3.6	3.67
Outlier Loss Ratio	3.11	3.53	2.95	2.99	3.06	3.0	3.26	3.19	2.95	2.77	...	2.71	2.74	2.68	2.89	2.82	2.77	2.76	3.23	2.94	3.6
MTD	0.01	0.01	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.01	...	0.01	0.01	0.01	0.01	0.01	0.0	0.01	0.01	0.01	0.02
3M	-0.0	-0.07	-0.01	-0.01	-0.07	-0.07	0.06	0.06	-0.01	-0.17	...	-0.03	-0.02	-0.06	-0.08	0.04	0.02	-0.03	0.1	0.3	0.13
6M	0.12	0.08	0.14	0.14	0.08	0.03	0.22	0.22	0.14	-0.02	...	0.11	0.13	0.08	0.03	0.17	0.13	0.11	-0.06	-0.1	-0.05
YTD	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
1Y	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
3Y (ann.)	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
5Y (ann.)	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
10Y (ann.)	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
All-time (ann.)	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
Best Day	0.04	0.06	0.06	0.06	0.06	0.06	0.06	0.06	0.06	0.06	...	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.05	0.14	0.04
Worst Day	-0.03	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.05	...	-0.03	-0.03	-0.04	-0.03	-0.03	-0.03	-0.03	-0.05	-0.07	-0.07
Best Month	0.08	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.25	...	0.07	0.07	0.07	0.07	0.08	0.08	0.07	0.08	0.21	0.09
Worst Month	-0.05	-0.08	-0.06	-0.03	-0.08	-0.08	-0.07	-0.06	-0.05	-0.11	...	-0.04	-0.04	-0.08	-0.07	-0.07	-0.06	-0.04	-0.09	-0.22	-0.11
Best Year	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
Worst Year	0.07	-0.04	0.09	0.11	-0.08	-0.05	0.08	0.11	0.09	0.07	...	0.1	0.13	-0.05	0.02	0.17	0.17	0.13	-0.11	-0.15	-0.06
Avg. Drawdown	-0.06	-0.09	-0.09	-0.08	-0.15	-0.06	-0.05	-0.06	-0.11	-0.07	...	-0.05	-0.05	-0.15	-0.06	-0.04	-0.04	-0.05	-0.09	-0.15	-0.15
Avg. Drawdown Days	69	69	85	84	178	71	58	49	114	37	...	42	42	176	49	33	37	42	89	88	118
Recovery Factor	0.57	-0.21	0.69	0.97	-0.52	-0.27	0.56	0.9	0.81	0.25	...	0.97	1.16	-0.3	0.16	1.23	1.39	1.2	-0.35	-0.3	-0.21
Ulcer Index	1.06	1.07	inf	inf	inf	inf	inf	inf	inf	1.04	...	1.04	1.03	1.06	1.04	1.03	1.03	1.03	1.04	1.02	1.08
Avg. Up Month	0.04	0.04	0.04	0.05	0.05	0.05	0.05	0.05	0.04	0.1	...	0.04	0.03	0.04	0.05	0.03	0.03	0.03	0.04	0.11	0.04

60 rows × 28 columns

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Portfolio D

1. Portfolio D: Long and Multiple Configured LSTM Trade Strategies for US Indixes

   Description:

    1. Portfolio with 27 trading strategies
    2. $200,000/strategy, Total Budget = $5.4M
    3. No-Rebalance during the backtest 
    4. 100% capital invested
    5. Portfolio investment using equal weights for each trade strategy
   
   

   Trade Strategies:

    1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold
    2. 21 trade strategies: Nasdaq, S&P 500, and DOW Jones using following LSTM Configurations:
   
                                                   LSTM Models
          Parameter                 #1      #2     #3       #4      #5      #6      #7
   
       epochs                       20      20     20       20      20      20      20
       no neurons layer 1           20      20     20       20      20      20      20
       no neurons layer 2           20      20     20       20      20      20      20
       time series length           20     100     50       50      40      30      60         
       no predictions ahead          3      15      5        5       3       3       5
       extend trade dyn            Yes     Yes    Yes      Yes     Yes     Yes     Yes
       re_evalaute_exit_dyn        Yes     Yes    Yes      Yes     Yes     Yes     Yes
       enter_if_last_nmatch          1       1      1        1       1       1       1
       enter_if_cross_over_sig      No      No     No       No      No      No      No
       exit_if_last_nmatch           1       1      1        2       1       1       1
   
    3. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using the following Composite 
       model configuration: 
   
        - A Composite LSTM Trade Strategy based on all the LSTM configured trade strategies above
   
        - The Set Entry cofiguration rules are: 
          a. Majority Voting (default)
          b. Enter a trade if the last n voting decisions are the same, set to 1 (default )
          c. Enter a trade if there is cross-over signaled after applying rules a) and b), 
             set to False (default)
   
        - The Set Exit configuration rules are: 
          a. Majority Voting (Default)
              - Exit a long trade if the percent of LSTM Indicators signaling an exit for a long position
                 is greater that the percent signaling no-exit 
              - Exit a short trade if the percent of LSTM Indicators signaling an exit for a short position
                 is greater that the percent signaling no-exit 
          b. Exit a trade if the last n exit decisions are the same, set to 1 (default )
   
   

   Backtest:

    1. Type: regular
    2. AI Models Fit Date Ranges
        1. Training Period: 
            1. Begin Date = 2001-01-01 
            2. End Date   = 2005-12-31  
        2. Validation Period: 
            1. Begin Date = 2006-01-01 
            2. End Date   = 2007-06-30   
    4. Test Period: 
        1. Begin Date = 2008-01-01 
        2. End Date   = 2008-12-31         

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 15:44:35,256 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_1",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_2",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 100, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 15,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_3",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_4",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_5",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 40, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #6

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_6",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 30, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #7

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_7",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 60, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = None )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = None )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  24

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Multiple-LSTM  TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio D, US-Indexes with Multiple-LSTM and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 15:48:18,655 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio D, US-Indexes with Multiple-LSTM and Long strategies" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

2021-10-28 15:48:30,980 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 15:48:30,981 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 15:48:30,982 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 15:48:30,982 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 15:48:30,983 [INFO] root: PortfolioRegisterItem init called. 
Portfolio of US-Indexes using SMA and Long strategies
Portfolio of US-Indexes using LSTM and Long strategies
Portfolio of US-Indexes with Multiple-SMA and Long strategies
Portfolio of US-Indexes with Multiple-SMA and Long strategies
Portfolio of US-Indexes with Multiple-LSTM and Long strategies
5 Portfolio of US-Indexes with Multiple-LSTM and Long strategies

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2000-07-01",
                             strBegDate         = "2001-01-01",
                             strEndDate         = "2005-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-06-30" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2008-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio of Multiple-LSTM based Trade Strategies",
                    timePeriod  = testing_period,
                    fitDatasets = fit_datasets,
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']]

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes with Multiple-LSTM and...	Yes	880	54000000.0	8.098601e+07	0.499741	Completed
1	ts_lstm_1_^DJI	No	62	2000000.0	4.567614e+06	1.283807	Completed
2	ts_lstm_2_^DJI	No	44	2000000.0	3.582771e+06	0.791385	Completed
3	ts_lstm_3_^DJI	No	45	2000000.0	3.635315e+06	0.817657	Completed
4	ts_lstm_4_^DJI	No	25	2000000.0	2.175172e+06	0.087586	Completed
5	ts_lstm_5_^DJI	No	68	2000000.0	4.905520e+06	1.452760	Completed
6	ts_lstm_6_^DJI	No	60	2000000.0	4.447922e+06	1.223961	Completed
7	ts_lstm_7_^DJI	No	43	2000000.0	3.115092e+06	0.557546	Completed
8	all_^DJI	No	50	2000000.0	3.472202e+06	0.736101	Completed
9	ts_lstm_1_^IXIC	No	56	2000000.0	5.493883e+06	1.746941	Completed
10	ts_lstm_2_^IXIC	No	49	2000000.0	2.720286e+06	0.360143	Completed
11	ts_lstm_3_^IXIC	No	50	2000000.0	4.760641e+06	1.380321	Completed
12	ts_lstm_4_^IXIC	No	23	2000000.0	1.733701e+06	-0.133149	Completed
13	ts_lstm_5_^IXIC	No	54	2000000.0	4.355464e+06	1.177732	Completed
14	ts_lstm_6_^IXIC	No	54	2000000.0	4.423564e+06	1.211782	Completed
15	ts_lstm_7_^IXIC	No	55	2000000.0	4.932952e+06	1.466476	Completed
16	all_^IXIC	No	54	2000000.0	4.976641e+06	1.488320	Completed
17	ts_lstm_1_^GSPC	No	21	2000000.0	2.176975e+06	0.088488	Completed
18	ts_lstm_2_^GSPC	No	6	2000000.0	1.537192e+06	-0.231404	Completed
19	ts_lstm_3_^GSPC	No	5	2000000.0	1.466085e+06	-0.266957	Completed
20	ts_lstm_4_^GSPC	No	5	2000000.0	1.393045e+06	-0.303478	Completed
21	ts_lstm_5_^GSPC	No	21	2000000.0	2.184461e+06	0.092231	Completed
22	ts_lstm_6_^GSPC	No	17	2000000.0	2.029246e+06	0.014623	Completed
23	ts_lstm_7_^GSPC	No	3	2000000.0	1.466923e+06	-0.266538	Completed
24	all_^GSPC	No	7	2000000.0	1.628941e+06	-0.185530	Completed
25	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	1.248316e+06	-0.375842	Completed
26	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	1.209029e+06	-0.395486	Completed
27	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	1.347062e+06	-0.326469	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2008-01-03	2008-12-31	1.0	0.5	0.5	1.45	-0.22	0.11	-0.07
1	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2008-01-03	2008-12-31	1.0	1.28	1.29	2.37	-0.24	0.11	-0.07
2	ts_lstm_2_^DJI	No	ts_lstm_2	^DJI	2008-01-03	2008-12-31	1.0	0.79	0.8	1.72	-0.24	0.11	-0.08
3	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2008-01-03	2008-12-31	1.0	0.82	0.82	1.76	-0.22	0.11	-0.08
4	ts_lstm_4_^DJI	No	ts_lstm_4	^DJI	2008-01-03	2008-12-31	1.0	0.09	0.09	0.41	-0.28	0.11	-0.08
5	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2008-01-03	2008-12-31	1.0	1.45	1.46	2.57	-0.24	0.11	-0.07
6	ts_lstm_6_^DJI	No	ts_lstm_6	^DJI	2008-01-03	2008-12-31	1.0	1.22	1.23	2.3	-0.24	0.11	-0.07
7	ts_lstm_7_^DJI	No	ts_lstm_7	^DJI	2008-01-03	2008-12-31	1.0	0.56	0.56	1.35	-0.26	0.11	-0.08
8	all_^DJI	No	all	^DJI	2008-01-03	2008-12-31	1.0	0.74	0.74	1.64	-0.24	0.11	-0.08
9	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2008-01-03	2008-12-31	1.0	1.75	1.76	2.67	-0.23	0.12	-0.07
10	ts_lstm_2_^IXIC	No	ts_lstm_2	^IXIC	2008-01-03	2008-12-31	1.0	0.36	0.36	0.95	-0.33	0.12	-0.09
11	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2008-01-03	2008-12-31	1.0	1.38	1.39	2.31	-0.21	0.12	-0.07
12	ts_lstm_4_^IXIC	No	ts_lstm_4	^IXIC	2008-01-03	2008-12-31	1.0	-0.13	-0.13	-0.14	-0.26	0.12	-0.07
13	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2008-01-03	2008-12-31	1.0	1.18	1.19	2.09	-0.25	0.12	-0.07
14	ts_lstm_6_^IXIC	No	ts_lstm_6	^IXIC	2008-01-03	2008-12-31	1.0	1.21	1.22	2.13	-0.25	0.12	-0.07
15	ts_lstm_7_^IXIC	No	ts_lstm_7	^IXIC	2008-01-03	2008-12-31	1.0	1.47	1.48	2.4	-0.23	0.12	-0.07
16	all_^IXIC	No	all	^IXIC	2008-01-03	2008-12-31	1.0	1.49	1.5	2.43	-0.23	0.12	-0.07
17	ts_lstm_1_^GSPC	No	ts_lstm_1	^GSPC	2008-01-03	2008-12-31	1.0	0.09	0.09	0.41	-0.26	0.12	-0.09
18	ts_lstm_2_^GSPC	No	ts_lstm_2	^GSPC	2008-01-03	2008-12-31	1.0	-0.23	-0.23	-0.44	-0.41	0.12	-0.09
19	ts_lstm_3_^GSPC	No	ts_lstm_3	^GSPC	2008-01-03	2008-12-31	1.0	-0.27	-0.27	-0.55	-0.48	0.12	-0.09
20	ts_lstm_4_^GSPC	No	ts_lstm_4	^GSPC	2008-01-03	2008-12-31	1.0	-0.3	-0.3	-0.68	-0.48	0.12	-0.09
21	ts_lstm_5_^GSPC	No	ts_lstm_5	^GSPC	2008-01-03	2008-12-31	1.0	0.09	0.09	0.42	-0.26	0.12	-0.09
22	ts_lstm_6_^GSPC	No	ts_lstm_6	^GSPC	2008-01-03	2008-12-31	1.0	0.01	0.01	0.24	-0.32	0.12	-0.09
23	ts_lstm_7_^GSPC	No	ts_lstm_7	^GSPC	2008-01-03	2008-12-31	1.0	-0.27	-0.27	-0.55	-0.48	0.12	-0.09
24	all_^GSPC	No	all	^GSPC	2008-01-03	2008-12-31	1.0	-0.19	-0.19	-0.3	-0.42	0.12	-0.09
25	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2008-01-03	2008-12-31	1.0	-0.38	-0.38	-0.94	-0.48	0.12	-0.09
26	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2008-12-31	1.0	-0.4	-0.4	-1.02	-0.49	0.12	-0.09
27	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2008-12-31	1.0	-0.33	-0.33	-0.86	-0.42	0.11	-0.08

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0	1	2	3	4	5	6	7	8	9	...	18	19	20	21	22	23	24	25	26	27
ta_reg_item_name	Portfolio of US-Indexes with Multiple-LSTM and...	ts_lstm_1_^DJI	ts_lstm_2_^DJI	ts_lstm_3_^DJI	ts_lstm_4_^DJI	ts_lstm_5_^DJI	ts_lstm_6_^DJI	ts_lstm_7_^DJI	all_^DJI	ts_lstm_1_^IXIC	...	ts_lstm_2_^GSPC	ts_lstm_3_^GSPC	ts_lstm_4_^GSPC	ts_lstm_5_^GSPC	ts_lstm_6_^GSPC	ts_lstm_7_^GSPC	all_^GSPC	Long-Only Trade Strategy for ^GSPC	Long-Only Trade Strategy for ^IXIC	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	5	67	68	69	70	71	72	73	74	75	...	84	85	86	87	88	89	90	91	92	93
is_portfolio	Yes	No	No	No	No	No	No	No	No	No	...	No	No	No	No	No	No	No	No	No	No
model_type		ts_lstm_1	ts_lstm_2	ts_lstm_3	ts_lstm_4	ts_lstm_5	ts_lstm_6	ts_lstm_7	all	ts_lstm_1	...	ts_lstm_2	ts_lstm_3	ts_lstm_4	ts_lstm_5	ts_lstm_6	ts_lstm_7	all	long	long	long
trade_asset		^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^IXIC	...	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^IXIC	^DJI
test_period_len_days	365	365	365	365	365	365	365	365	365	365	...	365	365	365	365	365	365	365	365	365	365
tr_period_len_days	1825	1825	1825	1825	1825	1825	1825	1825	1825	1825	...	1825	1825	1825	1825	1825	1825	1825	1825	1825	1825
val_period_len_days	545	545	545	545	545	545	545	545	545	545	...	545	545	545	545	545	545	545	545	545	545
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	...	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	...	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03
End Period	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	...	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
Cumulative Return	0.5	1.28	0.79	0.82	0.09	1.45	1.22	0.56	0.74	1.75	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
CAGR%	0.5	1.29	0.8	0.82	0.09	1.46	1.23	0.56	0.74	1.76	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
Sharpe	1.45	2.37	1.72	1.76	0.41	2.57	2.3	1.35	1.64	2.67	...	-0.44	-0.55	-0.68	0.42	0.24	-0.55	-0.3	-0.94	-1.02	-0.86
Sortino	2.5	4.08	2.8	2.98	0.64	4.42	3.95	2.22	2.65	4.81	...	-0.62	-0.79	-0.96	0.65	0.37	-0.79	-0.43	-1.29	-1.4	-1.2
Sortino/√2	1.76	2.88	1.98	2.11	0.45	3.12	2.79	1.57	1.87	3.4	...	-0.44	-0.56	-0.68	0.46	0.26	-0.56	-0.3	-0.91	-0.99	-0.85
Max Drawdown	-0.22	-0.24	-0.24	-0.22	-0.28	-0.24	-0.24	-0.26	-0.24	-0.23	...	-0.41	-0.48	-0.48	-0.26	-0.32	-0.48	-0.42	-0.48	-0.49	-0.42
Longest DD Days	65	51	78	63	92	48	48	154	71	54	...	362	362	362	126	225	362	362	362	362	240
Volatility (ann.)	0.31	0.38	0.38	0.38	0.38	0.38	0.38	0.38	0.38	0.41	...	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.41	0.38
Calmar	2.3	5.37	3.31	3.73	0.32	6.07	5.12	2.15	3.08	7.58	...	-0.57	-0.56	-0.64	0.36	0.05	-0.56	-0.44	-0.79	-0.8	-0.78
Skew	1.36	0.7	0.54	0.75	0.82	0.68	0.71	0.71	0.53	0.8	...	0.21	0.45	0.44	0.75	0.73	0.45	0.47	0.18	0.21	0.43
Kurtosis	8.96	3.88	4.0	3.92	4.19	3.91	3.88	3.98	3.98	2.75	...	3.96	4.01	4.04	3.95	3.98	4.01	3.97	3.99	3.22	4.22
Expected Daily %	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	-0.0	-0.0	-0.0	0.0	0.0	-0.0	-0.0	-0.0	-0.0	-0.0
Expected Monthly %	0.03	0.07	0.05	0.05	0.01	0.08	0.07	0.04	0.05	0.09	...	-0.02	-0.03	-0.03	0.01	0.0	-0.03	-0.02	-0.04	-0.04	-0.03
Expected Yearly %	0.5	1.28	0.79	0.82	0.09	1.45	1.22	0.56	0.74	1.75	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
Kelly Criterion	0.16	0.2	0.15	0.15	0.04	0.23	0.2	0.12	0.14	0.22	...	-0.04	-0.06	-0.07	0.04	0.02	-0.05	-0.03	-0.1	-0.09	-0.08
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.03	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	...	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Expected Shortfall (cVaR)	-0.03	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	...	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Gain/Pain Ratio	0.4	0.56	0.38	0.39	0.08	0.61	0.54	0.29	0.36	0.62	...	-0.08	-0.1	-0.12	0.08	0.05	-0.1	-0.05	-0.16	-0.17	-0.15
Gain/Pain (1M)	3.33	46.57	2.83	4.82	0.59	inf	45.25	1.78	3.45	6.99	...	-0.43	-0.46	-0.58	0.72	0.37	-0.45	-0.29	-0.81	-0.69	-0.82
Payoff Ratio	1.05	1.17	1.16	1.22	1.04	1.12	1.15	1.17	1.12	1.22	...	0.86	0.88	0.86	0.99	0.97	0.89	0.91	0.83	0.96	0.92
Profit Factor	1.4	1.56	1.38	1.39	1.08	1.61	1.54	1.29	1.36	1.62	...	0.92	0.9	0.88	1.08	1.05	0.9	0.95	0.84	0.83	0.85
Common Sense Ratio	2.01	2.07	1.6	1.81	1.32	2.14	2.04	1.54	1.6	2.3	...	0.97	0.89	0.87	1.25	1.13	0.89	1.0	0.76	0.77	0.74
CPC Index	0.84	1.04	0.87	0.9	0.57	1.07	1.01	0.79	0.83	1.13	...	0.41	0.4	0.38	0.56	0.53	0.41	0.44	0.35	0.37	0.38
Tail Ratio	1.44	1.33	1.16	1.31	1.23	1.33	1.33	1.19	1.18	1.42	...	1.06	0.99	0.99	1.16	1.08	0.99	1.06	0.91	0.92	0.86
Outlier Win Ratio	6.45	4.63	4.32	4.35	4.54	4.73	4.66	4.42	4.38	4.7	...	4.18	4.92	4.94	5.44	5.49	4.88	4.84	4.26	3.61	4.23
Outlier Loss Ratio	5.24	3.77	4.19	3.65	3.55	3.67	3.74	3.74	4.11	3.26	...	4.36	3.85	3.81	3.64	3.61	3.88	3.91	4.3	4.04	4.23
MTD	0.27	0.38	0.29	0.3	0.04	0.38	0.36	0.3	0.38	0.26	...	0.01	0.21	0.12	0.22	0.22	0.21	0.21	0.01	0.03	-0.01
3M	0.17	0.5	0.14	0.19	-0.14	0.5	0.48	0.07	0.23	0.23	...	-0.0	-0.04	-0.09	0.08	0.08	-0.04	0.07	-0.18	-0.2	-0.15
6M	0.39	0.91	0.62	0.81	0.08	0.89	0.85	0.62	0.6	0.97	...	-0.14	-0.17	-0.22	0.13	0.01	-0.17	-0.08	-0.29	-0.31	-0.23
YTD	0.5	1.28	0.79	0.82	0.09	1.45	1.22	0.56	0.74	1.75	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
1Y	0.5	1.28	0.79	0.82	0.09	1.45	1.22	0.56	0.74	1.75	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
3Y (ann.)	0.5	1.29	0.8	0.82	0.09	1.46	1.23	0.56	0.74	1.76	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
5Y (ann.)	0.5	1.29	0.8	0.82	0.09	1.46	1.23	0.56	0.74	1.76	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
10Y (ann.)	0.5	1.29	0.8	0.82	0.09	1.46	1.23	0.56	0.74	1.76	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
All-time (ann.)	0.5	1.29	0.8	0.82	0.09	1.46	1.23	0.56	0.74	1.76	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
Best Day	0.11	0.11	0.11	0.11	0.11	0.11	0.11	0.11	0.11	0.12	...	0.12	0.12	0.12	0.12	0.12	0.12	0.12	0.12	0.12	0.11
Worst Day	-0.07	-0.07	-0.08	-0.08	-0.08	-0.07	-0.07	-0.08	-0.08	-0.07	...	-0.09	-0.09	-0.09	-0.09	-0.09	-0.09	-0.09	-0.09	-0.09	-0.08
Best Month	0.27	0.38	0.29	0.3	0.1	0.38	0.36	0.3	0.38	0.44	...	0.13	0.21	0.12	0.22	0.22	0.21	0.21	0.05	0.06	0.05
Worst Month	-0.08	-0.02	-0.15	-0.1	-0.14	-0.01	-0.02	-0.12	-0.15	-0.14	...	-0.17	-0.17	-0.17	-0.14	-0.14	-0.17	-0.17	-0.17	-0.18	-0.14
Best Year	0.5	1.28	0.79	0.82	0.09	1.45	1.22	0.56	0.74	1.75	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
Worst Year	0.5	1.28	0.79	0.82	0.09	1.45	1.22	0.56	0.74	1.75	...	-0.23	-0.27	-0.3	0.09	0.01	-0.27	-0.19	-0.38	-0.4	-0.33
Avg. Drawdown	-0.02	-0.04	-0.04	-0.04	-0.05	-0.04	-0.03	-0.05	-0.04	-0.04	...	-0.41	-0.48	-0.48	-0.06	-0.05	-0.48	-0.42	-0.48	-0.49	-0.26
Avg. Drawdown Days	12	8	15	18	24	8	8	24	15	8	...	362	362	362	30	27	362	362	362	362	180
Recovery Factor	2.29	5.32	3.28	3.7	0.32	6.02	5.08	2.13	3.06	7.51	...	-0.57	-0.56	-0.63	0.36	0.05	-0.56	-0.44	-0.78	-0.8	-0.78
Ulcer Index	4.92	1.79	1.81	1.8	1.82	1.79	1.79	1.81	1.8	1.45	...	inf	inf	inf	inf	inf	inf	inf	inf	1.56	1.86
Avg. Up Month	0.08	0.09	0.12	0.11	0.06	0.09	0.09	0.11	0.11	0.15	...	0.04	0.07	0.05	0.06	0.06	0.07	0.06	0.02	0.03	0.02

60 rows × 28 columns

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Portfolio E

1. Portfolio E: Long and Eleven Distinct Technical Analysis Based Trade Strategies for US Indixes

   Description:

    1. Portfolio with 39 trading strategies
    2. $200,000/strategy, Total Budget = $7.8M
    3. No-Rebalance during the backtest 
    4. 100% capital invested
    5. Portfolio investment using equal weights for each trade strategy
   
   

   Trade Strategies:

    1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold
   
    2. 33 trade strategies: Nasdaq, S&P 500, and DOW Jones using following TA Indicators & Configurations:
   
       See: https://technical-analysis-library-in-python.readthedocs.io/en/latest/index.html for 
       explanation on the technical indicator specific parameters
   
            TS-No    TA-Indicator                 Parameters
   
              1           sma           fast_mav                      10 
                                        slow_mav                      50 
                                        enter_if_last_nmatch           2 
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1
   
              2            bb           window                        20
                                        window_dev                     1
                                        enter_if_last_nmatch           2 
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1     
   
              3          macd           window_slow                   26
                                        window_fast                   12
                                        window_sign                    9
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
              4           rsi           enter_long if rsi  <=         30
                                        exit_long  if rsi  >          30
                                        enter_short if rsi >=         70
                                        exit_short if rsi  <=         60
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
              5          srsi           window                        14
                                        smooth1                        3
                                        smooth2                        3
                                        enter_long if stochrsi_d >=  0.5
                                        exit_long if stochrsi_d  <   0.5
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
              6           adx           window                        14
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
              7         mkama           kama_fast_window              10
                                        kama_fast_pow1                 2
                                        kama_fast_pow1                30
                                        kama_slow_window              10
                                        kama_slow_pow1                 5
                                        kama_slow_pow1                30
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
              8           tsi           window_slow                   25
                                        window_fast                    5
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
              9      forceidx           window                        13
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
             10           mfi           window                        14
                                        no mfi points for slope        5
                                        enter_if_last_nmatch           2
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1 
   
             11           sma           fast_mav                      15 
                                        slow_mav                      60 
                                        enter_if_last_nmatch           2 
                                        enter_if_cross_over_sig      Yes
                                        exit_if_last_nmatch            1
   
    3. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using the following configuration: 
   
        - A Composite Trade Strategy based on all the technical-analysis indicators configured 
          above
   
        - The Set Entry cofiguration rules are: 
          a. Majority Voting (default)
          b. Enter a trade if the last n voting decisions are the same, set to 1 (default )
          c. Enter a trade if there is cross-over signaled after applying rules a) and b), 
             set to False
   
        - The Set Exit configuration rules are: 
          a. Majority Voting (Default)
          b. Exit a trade if the last n exit decisions are the same, set to 1 (default )
   
   

   Backtest:

    1. Type: regular
    2. Test Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2010-12-31 

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 16:40:59,791 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )          
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_1",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_bb",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "bb",
                                  'type'     : "ta",
                                  'obj_args' : { 'window' : 13, 'window_dev' : 1 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_macd",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "macd",
                                  'type'     : "ta",
                                  'obj_args' : { 'window_slow' : 26, 'window_fast' : 12, 'window_sign' : 9,
                                                 'enter_long_macd_diff'  : 0.5 , 'exi_long_macd_diff' : 0.5,
                                                 'enter_short_macd_diff' : -0.5, 'exi_short_macd_diff' : -0.5  },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_rsi",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "rsi",
                                  'type'     : "ta",
                                  'obj_args' : { 'rsi_enter_long'  : 30.0,  'rsi_exit_long'  : 30.0, 
                                                 'rsi_enter_short' : 70.0,  'rsi_exit_short' : 70.0 },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #6

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_srsi",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "srsi",
                                  'type'     : "ta",
                                  'obj_args' : { 'window' : 14,  'smooth1' : 3, 'smooth2'  : 3,
                                                 'srsi_enter_long'  : 0.8,  'srsi_exit_long'  : 0.8,
                                                 'srsi_enter_short' : 0.2,  'srsi_exit_short' : 0.2 },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #7

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_adx",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "adx",
                              'type'     : "ta",
                              'obj_args' : { 'window' : 14, 'adx_enter_long_ge' : 14, 'adx_exit_long_lt'  : 14,
                                             'adx_enter_short_ge' : 14, 'adx_exit_short_lt' : 14 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #8

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_kama",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "kama",
                              'type'     : "ta",
                              'obj_args' : { 'window_fast' : 10, 'pow1_fast' : 2, 'pow2_fast'  : 30,
                                             'window_slow' : 10, 'pow1_slow' : 5, 'pow2_slow'  : 30 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #9

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_tsi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "tsi",
                              'type'     : "ta",
                              'obj_args' : { 'window_slow' : 25, 'window_fast' : 5 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

# TS #10

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_forceindex",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "forceindex",
                              'type'     : "ta",
                              'obj_args' : { 'window' : 39 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

# TS #11

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_mfi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "mfi",
                              'type'     : "ta",
                              'obj_args' : { 'window' : 14, 'long_slope_win' : 15, 'short_slope_win' : 15 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = None )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = None )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured TA Register Items : ", len(taRegisterItems ))

Number of configured TA Register Items :  36

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Distinct TechAnal TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with Distinct TechAnal & Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 16:41:49,255 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio of Distinct TechAnal & Long Strategies",
                    timePeriod = DatasetDef(datasetType = DatasetType.Testing,
                                            strBegDate = "2006-01-01", 
                                            strEndDate = "2010-12-31" ),
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']]

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes with Distinct TechAnal...	Yes	3885	78000000.0	8.914324e+07	0.142862	Completed
1	ts_sma_1_^DJI	No	27	2000000.0	3.259475e+06	0.629737	Completed
2	ts_sma_2_^DJI	No	21	2000000.0	2.715084e+06	0.357542	Completed
3	ts_bb_^DJI	No	230	2000000.0	2.425231e+06	0.212616	Completed
4	ts_macd_^DJI	No	107	2000000.0	2.034301e+06	0.017151	Completed
5	ts_rsi_^DJI	No	48	2000000.0	2.665718e+06	0.332859	Completed
6	ts_srsi_^DJI	No	208	2000000.0	1.628879e+06	-0.185561	Completed
7	ts_adx_^DJI	No	97	2000000.0	2.446447e+06	0.223224	Completed
8	ts_kama_^DJI	No	19	2000000.0	2.527645e+06	0.263822	Completed
9	ts_tsi_^DJI	No	58	2000000.0	2.140794e+06	0.070397	Completed
10	ts_forceindex_^DJI	No	77	2000000.0	1.792767e+06	-0.103617	Completed
11	ts_mfi_^DJI	No	182	2000000.0	2.043748e+06	0.021874	Completed
12	all_^DJI	No	181	2000000.0	2.364100e+06	0.182050	Completed
13	ts_sma_1_^IXIC	No	28	2000000.0	3.096447e+06	0.548224	Completed
14	ts_sma_2_^IXIC	No	23	2000000.0	2.653684e+06	0.326842	Completed
15	ts_bb_^IXIC	No	201	2000000.0	1.750279e+06	-0.124861	Completed
16	ts_macd_^IXIC	No	125	2000000.0	1.611098e+06	-0.194451	Completed
17	ts_rsi_^IXIC	No	42	2000000.0	1.653055e+06	-0.173473	Completed
18	ts_srsi_^IXIC	No	229	2000000.0	1.261079e+06	-0.369461	Completed
19	ts_adx_^IXIC	No	98	2000000.0	3.130286e+06	0.565143	Completed
20	ts_kama_^IXIC	No	22	2000000.0	2.632824e+06	0.316412	Completed
21	ts_tsi_^IXIC	No	55	2000000.0	2.302845e+06	0.151422	Completed
22	ts_forceindex_^IXIC	No	84	2000000.0	2.625037e+06	0.312519	Completed
23	ts_mfi_^IXIC	No	183	2000000.0	1.670405e+06	-0.164798	Completed
24	all_^IXIC	No	197	2000000.0	3.222387e+06	0.611194	Completed
25	ts_sma_1_^GSPC	No	28	2000000.0	2.676540e+06	0.338270	Completed
26	ts_sma_2_^GSPC	No	22	2000000.0	2.906510e+06	0.453255	Completed
27	ts_bb_^GSPC	No	219	2000000.0	2.725456e+06	0.362728	Completed
28	ts_macd_^GSPC	No	169	2000000.0	1.607609e+06	-0.196196	Completed
29	ts_rsi_^GSPC	No	55	2000000.0	2.560077e+06	0.280038	Completed
30	ts_srsi_^GSPC	No	231	2000000.0	1.097250e+06	-0.451375	Completed
31	ts_adx_^GSPC	No	96	2000000.0	2.403768e+06	0.201884	Completed
32	ts_kama_^GSPC	No	15	2000000.0	2.867114e+06	0.433557	Completed
33	ts_tsi_^GSPC	No	66	2000000.0	1.832013e+06	-0.083994	Completed
34	ts_forceindex_^GSPC	No	72	2000000.0	2.156671e+06	0.078335	Completed
35	ts_mfi_^GSPC	No	169	2000000.0	1.331140e+06	-0.334430	Completed
36	all_^GSPC	No	198	2000000.0	2.844191e+06	0.422095	Completed
37	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	1.982412e+06	-0.008794	Completed
38	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	2.364535e+06	0.182267	Completed
39	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	2.134338e+06	0.067169	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio of US-Indexes with Distinct TechAnal...	Yes			2006-01-04	2010-12-31	1.0	0.14	0.03	0.33	-0.1	0.04	-0.03
1	ts_sma_1_^DJI	No	ts_sma_1	^DJI	2006-01-04	2010-12-31	0.98	0.63	0.1	0.6	-0.24	0.07	-0.07
2	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2006-01-04	2010-12-31	0.99	0.36	0.06	0.44	-0.2	0.06	-0.06
3	ts_bb_^DJI	No	ts_bb	^DJI	2006-01-04	2010-12-31	0.7	0.21	0.04	0.3	-0.22	0.11	-0.08
4	ts_macd_^DJI	No	ts_macd	^DJI	2006-01-04	2010-12-31	0.93	0.02	0.0	0.12	-0.24	0.07	-0.08
5	ts_rsi_^DJI	No	ts_rsi	^DJI	2006-01-04	2010-12-31	0.09	0.33	0.06	0.76	-0.08	0.11	-0.03
6	ts_srsi_^DJI	No	ts_srsi	^DJI	2006-01-04	2010-12-31	0.5	-0.19	-0.04	-0.22	-0.35	0.08	-0.11
7	ts_adx_^DJI	No	ts_adx	^DJI	2006-01-04	2010-12-31	0.89	0.22	0.04	0.32	-0.21	0.07	-0.07
8	ts_kama_^DJI	No	ts_kama	^DJI	2006-01-04	2010-12-31	1.0	0.26	0.05	0.36	-0.27	0.05	-0.05
9	ts_tsi_^DJI	No	ts_tsi	^DJI	2006-01-04	2010-12-31	0.96	0.07	0.01	0.17	-0.25	0.07	-0.07
10	ts_forceindex_^DJI	No	ts_forceindex	^DJI	2006-01-04	2010-12-31	0.92	-0.1	-0.02	-0.02	-0.31	0.06	-0.08
11	ts_mfi_^DJI	No	ts_mfi	^DJI	2006-01-04	2010-12-31	0.58	0.02	0.0	0.11	-0.31	0.07	-0.09
12	all_^DJI	No	all	^DJI	2006-01-04	2010-12-31	0.97	0.18	0.03	0.27	-0.22	0.08	-0.06
13	ts_sma_1_^IXIC	No	ts_sma_1	^IXIC	2006-01-04	2010-12-31	0.98	0.55	0.09	0.51	-0.3	0.09	-0.07
14	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2006-01-04	2010-12-31	0.99	0.33	0.06	0.37	-0.31	0.09	-0.07
15	ts_bb_^IXIC	No	ts_bb	^IXIC	2006-01-04	2010-12-31	0.67	-0.12	-0.03	-0.02	-0.33	0.12	-0.07
16	ts_macd_^IXIC	No	ts_macd	^IXIC	2006-01-04	2010-12-31	0.92	-0.19	-0.04	-0.07	-0.31	0.08	-0.09
17	ts_rsi_^IXIC	No	ts_rsi	^IXIC	2006-01-04	2010-12-31	0.09	-0.17	-0.04	-0.34	-0.27	0.12	-0.07
18	ts_srsi_^IXIC	No	ts_srsi	^IXIC	2006-01-04	2010-12-31	0.52	-0.37	-0.09	-0.43	-0.43	0.09	-0.11
19	ts_adx_^IXIC	No	ts_adx	^IXIC	2006-01-04	2010-12-31	0.85	0.57	0.09	0.53	-0.27	0.09	-0.07
20	ts_kama_^IXIC	No	ts_kama	^IXIC	2006-01-04	2010-12-31	1.0	0.32	0.06	0.37	-0.3	0.08	-0.07
21	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2006-01-04	2010-12-31	0.96	0.15	0.03	0.24	-0.33	0.08	-0.06
22	ts_forceindex_^IXIC	No	ts_forceindex	^IXIC	2006-01-04	2010-12-31	0.92	0.31	0.06	0.36	-0.31	0.09	-0.07
23	ts_mfi_^IXIC	No	ts_mfi	^IXIC	2006-01-04	2010-12-31	0.58	-0.16	-0.04	-0.1	-0.39	0.09	-0.08
24	all_^IXIC	No	all	^IXIC	2006-01-04	2010-12-31	0.98	0.61	0.1	0.55	-0.25	0.08	-0.06
25	ts_sma_1_^GSPC	No	ts_sma_1	^GSPC	2006-01-04	2010-12-31	0.98	0.34	0.06	0.39	-0.26	0.09	-0.07
26	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2006-01-04	2010-12-31	0.99	0.45	0.08	0.48	-0.25	0.07	-0.06
27	ts_bb_^GSPC	No	ts_bb	^GSPC	2006-01-04	2010-12-31	0.68	0.36	0.06	0.4	-0.24	0.12	-0.09
28	ts_macd_^GSPC	No	ts_macd	^GSPC	2006-01-04	2010-12-31	0.89	-0.2	-0.04	-0.09	-0.34	0.08	-0.09
29	ts_rsi_^GSPC	No	ts_rsi	^GSPC	2006-01-04	2010-12-31	0.08	0.28	0.05	0.64	-0.09	0.12	-0.08
30	ts_srsi_^GSPC	No	ts_srsi	^GSPC	2006-01-04	2010-12-31	0.49	-0.45	-0.11	-0.65	-0.48	0.09	-0.1
31	ts_adx_^GSPC	No	ts_adx	^GSPC	2006-01-04	2010-12-31	0.87	0.2	0.04	0.28	-0.28	0.09	-0.07
32	ts_kama_^GSPC	No	ts_kama	^GSPC	2006-01-04	2010-12-31	1.0	0.43	0.07	0.5	-0.25	0.06	-0.05
33	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2006-01-04	2010-12-31	0.94	-0.08	-0.02	0.02	-0.29	0.08	-0.07
34	ts_forceindex_^GSPC	No	ts_forceindex	^GSPC	2006-01-04	2010-12-31	0.92	0.08	0.02	0.18	-0.31	0.09	-0.07
35	ts_mfi_^GSPC	No	ts_mfi	^GSPC	2006-01-04	2010-12-31	0.61	-0.33	-0.08	-0.31	-0.54	0.09	-0.09
36	all_^GSPC	No	all	^GSPC	2006-01-04	2010-12-31	0.97	0.42	0.07	0.45	-0.26	0.08	-0.06
37	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2006-01-04	2010-12-31	1.0	-0.01	-0.0	0.12	-0.57	0.12	-0.09
38	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2006-01-04	2010-12-31	1.0	0.18	0.03	0.26	-0.56	0.12	-0.09
39	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2006-01-04	2010-12-31	1.0	0.07	0.01	0.17	-0.54	0.11	-0.08

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0	1	2	3	4	5	6	7	8	9	...	30	31	32	33	34	35	36	37	38	39
ta_reg_item_name	Portfolio of US-Indexes with Distinct TechAnal...	ts_sma_1_^DJI	ts_sma_2_^DJI	ts_bb_^DJI	ts_macd_^DJI	ts_rsi_^DJI	ts_srsi_^DJI	ts_adx_^DJI	ts_kama_^DJI	ts_tsi_^DJI	...	ts_srsi_^GSPC	ts_adx_^GSPC	ts_kama_^GSPC	ts_tsi_^GSPC	ts_forceindex_^GSPC	ts_mfi_^GSPC	all_^GSPC	Long-Only Trade Strategy for ^GSPC	Long-Only Trade Strategy for ^IXIC	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	None	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
is_portfolio	Yes	No	No	No	No	No	No	No	No	No	...	No	No	No	No	No	No	No	No	No	No
model_type		ts_sma_1	ts_sma_2	ts_bb	ts_macd	ts_rsi	ts_srsi	ts_adx	ts_kama	ts_tsi	...	ts_srsi	ts_adx	ts_kama	ts_tsi	ts_forceindex	ts_mfi	all	long	long	long
trade_asset		^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	...	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^IXIC	^DJI
test_period_len_days	1825	1825	1825	1825	1825	1825	1825	1825	1825	1825	...	1825	1825	1825	1825	1825	1825	1825	1825	1825	1825
tr_period_len_days	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
val_period_len_days	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	...	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	...	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04	2006-01-04
End Period	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	...	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31	2010-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	0.98	0.99	0.7	0.93	0.09	0.5	0.89	1.0	0.96	...	0.49	0.87	1.0	0.94	0.92	0.61	0.97	1.0	1.0	1.0
Cumulative Return	0.14	0.63	0.36	0.21	0.02	0.33	-0.19	0.22	0.26	0.07	...	-0.45	0.2	0.43	-0.08	0.08	-0.33	0.42	-0.01	0.18	0.07
CAGR%	0.03	0.1	0.06	0.04	0.0	0.06	-0.04	0.04	0.05	0.01	...	-0.11	0.04	0.07	-0.02	0.02	-0.08	0.07	-0.0	0.03	0.01
Sharpe	0.33	0.6	0.44	0.3	0.12	0.76	-0.22	0.32	0.36	0.17	...	-0.65	0.28	0.5	0.02	0.18	-0.31	0.45	0.12	0.26	0.17
Sortino	0.47	0.88	0.63	0.45	0.17	2.05	-0.29	0.46	0.52	0.24	...	-0.88	0.42	0.73	0.02	0.25	-0.44	0.66	0.16	0.37	0.24
Sortino/√2	0.33	0.62	0.45	0.32	0.12	1.45	-0.21	0.32	0.37	0.17	...	-0.63	0.3	0.51	0.02	0.18	-0.31	0.47	0.12	0.26	0.17
Max Drawdown	-0.1	-0.24	-0.2	-0.22	-0.24	-0.08	-0.35	-0.21	-0.27	-0.25	...	-0.48	-0.28	-0.25	-0.29	-0.31	-0.54	-0.26	-0.57	-0.56	-0.54
Longest DD Days	594	360	397	695	786	848	1814	661	879	534	...	1814	703	594	1043	770	794	596	1178	1156	1178
Volatility (ann.)	0.09	0.19	0.17	0.19	0.2	0.08	0.14	0.18	0.17	0.19	...	0.16	0.2	0.17	0.2	0.2	0.2	0.2	0.25	0.26	0.23
Calmar	0.27	0.44	0.31	0.18	0.01	0.77	-0.12	0.2	0.18	0.05	...	-0.24	0.13	0.3	-0.06	0.05	-0.15	0.28	-0.0	0.06	0.02
Skew	0.01	0.02	-0.03	0.81	0.06	12.67	-1.04	0.16	-0.05	0.25	...	-0.14	0.48	0.04	0.34	0.25	-0.04	0.33	0.02	0.05	0.26
Kurtosis	6.16	4.7	2.99	17.46	5.79	246.79	28.06	5.44	2.51	4.51	...	20.39	5.52	2.48	4.9	5.57	12.01	4.1	8.88	6.4	9.55
Expected Daily %	0.0	0.0	0.0	0.0	0.0	0.0	-0.0	0.0	0.0	0.0	...	-0.0	0.0	0.0	-0.0	0.0	-0.0	0.0	-0.0	0.0	0.0
Expected Monthly %	0.0	0.01	0.01	0.0	0.0	0.0	-0.0	0.0	0.0	0.0	...	-0.01	0.0	0.01	-0.0	0.0	-0.01	0.01	-0.0	0.0	0.0
Expected Yearly %	0.03	0.1	0.06	0.04	0.0	0.06	-0.04	0.04	0.05	0.01	...	-0.11	0.04	0.07	-0.02	0.02	-0.08	0.07	-0.0	0.03	0.01
Kelly Criterion	0.03	0.06	0.04	0.03	0.01	0.25	-0.03	0.03	0.03	0.02	...	-0.1	0.03	0.04	0.0	0.02	-0.04	0.04	0.01	0.03	0.02
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.01	-0.02	-0.02	-0.02	-0.02	-0.01	-0.01	-0.02	-0.02	-0.02	...	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.03	-0.03	-0.02
Expected Shortfall (cVaR)	-0.01	-0.02	-0.02	-0.02	-0.02	-0.01	-0.01	-0.02	-0.02	-0.02	...	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.03	-0.03	-0.02
Gain/Pain Ratio	0.07	0.12	0.08	0.08	0.02	0.91	-0.06	0.06	0.07	0.03	...	-0.17	0.06	0.09	0.0	0.03	-0.08	0.09	0.02	0.05	0.03
Gain/Pain (1M)	0.45	0.97	0.56	0.46	0.13	2.33	-0.18	0.36	0.39	0.18	...	-0.53	0.36	0.59	0.02	0.19	-0.25	0.6	0.14	0.26	0.21
Payoff Ratio	0.91	0.99	0.97	1.17	1.04	1.75	0.88	0.98	0.99	0.92	...	0.95	1.04	1.01	0.94	0.95	1.0	0.99	0.85	0.88	0.88
Profit Factor	1.07	1.12	1.08	1.08	1.02	1.91	0.94	1.06	1.07	1.03	...	0.83	1.06	1.09	1.0	1.03	0.92	1.09	1.02	1.05	1.03
Common Sense Ratio	1.09	1.08	1.04	1.42	1.04	NaN	0.92	1.06	1.0	1.02	...	0.72	1.09	1.08	0.94	0.98	0.87	1.08	0.95	0.99	0.99
CPC Index	0.52	0.59	0.56	0.6	0.53	1.74	0.43	0.54	0.55	0.5	...	0.37	0.56	0.57	0.49	0.51	0.44	0.56	0.48	0.5	0.49
Tail Ratio	1.03	0.96	0.96	1.32	1.01	NaN	0.98	1.0	0.94	0.99	...	0.87	1.03	0.99	0.94	0.95	0.94	1.0	0.93	0.94	0.96
Outlier Win Ratio	4.47	4.33	3.86	7.59	4.91	21.93	10.41	4.91	3.93	4.65	...	10.74	5.37	3.85	4.9	5.0	8.68	4.38	4.66	4.44	4.29
Outlier Loss Ratio	4.44	3.81	3.68	4.43	4.06	1.49	3.43	3.67	3.57	3.7	...	3.6	3.86	3.73	3.77	3.81	3.86	3.86	4.43	3.89	4.44
MTD	0.03	0.05	0.05	0.0	-0.01	0.0	0.02	-0.0	0.05	-0.01	...	-0.01	0.01	0.07	0.03	0.07	-0.04	0.02	0.07	0.06	0.05
3M	0.05	0.07	0.07	0.02	-0.03	0.01	0.01	0.05	0.07	0.0	...	-0.04	0.04	0.1	0.06	0.1	-0.02	0.05	0.1	0.12	0.07
6M	-0.02	-0.01	-0.08	0.02	-0.02	0.01	0.02	0.01	-0.14	-0.03	...	-0.06	-0.02	0.02	-0.02	-0.0	-0.07	-0.02	0.22	0.26	0.18
YTD	0.02	0.04	-0.1	-0.01	0.06	0.06	0.03	0.09	-0.15	-0.01	...	-0.08	0.17	0.04	0.03	0.14	-0.17	0.13	0.13	0.17	0.11
1Y	0.01	0.03	-0.11	-0.0	0.05	0.06	0.02	0.08	-0.16	-0.03	...	-0.09	0.16	0.03	0.02	0.12	-0.17	0.12	0.12	0.16	0.1
3Y (ann.)	0.06	0.16	0.11	0.01	0.02	0.13	-0.03	0.1	0.14	0.08	...	-0.13	0.12	0.21	0.03	0.09	-0.14	0.2	-0.05	-0.0	-0.05
5Y (ann.)	0.03	0.1	0.06	0.04	0.0	0.06	-0.04	0.04	0.05	0.01	...	-0.11	0.04	0.07	-0.02	0.02	-0.08	0.07	-0.0	0.03	0.01
10Y (ann.)	0.03	0.1	0.06	0.04	0.0	0.06	-0.04	0.04	0.05	0.01	...	-0.11	0.04	0.07	-0.02	0.02	-0.08	0.07	-0.0	0.03	0.01
All-time (ann.)	0.03	0.1	0.06	0.04	0.0	0.06	-0.04	0.04	0.05	0.01	...	-0.11	0.04	0.07	-0.02	0.02	-0.08	0.07	-0.0	0.03	0.01
Best Day	0.04	0.07	0.06	0.11	0.07	0.11	0.08	0.07	0.05	0.07	...	0.09	0.09	0.06	0.08	0.09	0.09	0.08	0.12	0.12	0.11
Worst Day	-0.03	-0.07	-0.06	-0.08	-0.08	-0.03	-0.11	-0.07	-0.05	-0.07	...	-0.1	-0.07	-0.05	-0.07	-0.07	-0.09	-0.06	-0.09	-0.09	-0.08
Best Month	0.08	0.14	0.11	0.08	0.2	0.18	0.17	0.14	0.1	0.13	...	0.06	0.15	0.11	0.15	0.16	0.25	0.14	0.09	0.12	0.09
Worst Month	-0.04	-0.09	-0.11	-0.1	-0.11	-0.03	-0.1	-0.11	-0.09	-0.13	...	-0.09	-0.07	-0.1	-0.12	-0.11	-0.28	-0.07	-0.17	-0.18	-0.14
Best Year	0.08	0.29	0.31	0.12	0.09	0.24	0.03	0.16	0.35	0.28	...	-0.03	0.17	0.38	0.08	0.14	0.05	0.31	0.23	0.44	0.19
Worst Year	-0.06	-0.08	-0.1	-0.06	-0.13	-0.04	-0.14	-0.11	-0.16	-0.15	...	-0.2	-0.16	-0.2	-0.15	-0.19	-0.17	-0.19	-0.38	-0.41	-0.34
Avg. Drawdown	-0.02	-0.03	-0.04	-0.04	-0.07	-0.02	-0.35	-0.05	-0.03	-0.05	...	-0.48	-0.06	-0.03	-0.19	-0.06	-0.06	-0.05	-0.03	-0.05	-0.03
Avg. Drawdown Days	65	35	46	58	127	138	1814	104	37	80	...	1814	104	40	605	128	88	60	52	67	46
Recovery Factor	1.41	2.67	1.77	0.96	0.07	4.32	-0.53	1.06	0.98	0.28	...	-0.94	0.71	1.73	-0.29	0.26	-0.62	1.59	-0.02	0.33	0.13
Ulcer Index	1.05	1.04	1.06	inf	inf	inf	1.06	1.04	1.06	1.06	...	1.05	1.05	1.05	1.06	1.06	inf	1.06	1.05	1.05	1.05
Avg. Up Month	0.01	0.03	0.03	0.02	0.04	0.03	0.03	0.04	0.03	0.03	...	0.02	0.04	0.03	0.04	0.04	0.03	0.04	0.03	0.05	0.03

60 rows × 40 columns

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Portfolio F

1. Portfolio F: Long and Multiple Configured AI-Modeled Trade Strategies for US Indixes

   Description:

    1. Portfolio with 30 trading strategies
    2. $200,000/strategy, Total Budget = $6M
    3. No-Rebalance during the backtest 
    4. 100% capital invested
    5. Portfolio investment using equal weights for each trade strategy
   
   

   Trade Strategies:

    1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold
    2. 24 trade strategies: Nasdaq, S&P 500, and DOW Jones using the following AI-Models
       and corresponding configurations:
   
                                                       Trading AI-Models
   
          Parameter                 #1      #2      #3        #4          #5        #6        #7
   
       ai-model                    GRU    LSTM Conv1d-GRU Conv1d-LSTM  Wavenet  Wavenet-GRU  GRU
       epochs                       20      20      20        20          20        20        20
       no neurons layer 1           20      20      20        20           -        20        20
       no neurons layer 2           20      20      20        20           -        20        20
       time series length           50      50      50        50          50        50        40         
       no predictions ahead          5       5       5         5           5         5         3
       extend trade dyn            Yes     Yes     Yes       Yes         Yes       Yes       Yes
       re_evalaute_exit_dyn        Yes     Yes     Yes       Yes         Yes       Yes       Yes
       enter_if_last_nmatch          1       1       1         1           1         1         1
       enter_if_cross_over_sig      No      No      No        No          No        No        No
       exit_if_last_nmatch           1       1       1         1           1         1         1
   
          Parameter                 #8              
   
       ai-model                 Wavenet-LSTM 
       epochs                        20           
       no neurons layer 1            20           
       no neurons layer 2            20           
       time series length            50                    
       no predictions ahead           5            
       extend trade dyn             Yes          
       re_evalaute_exit_dyn         Yes          
       enter_if_last_nmatch           1            
       enter_if_cross_over_sig       No           
       exit_if_last_nmatch            1            
   
       Note: 
         - the parameters "no neurons layer 1" and "no neurons layer 2" are only applicable to 
           AI Models that have GRU or LSTM layers. 
         - Conv1d is a convulation layer in one dimension (configured with 40 filters )
         - Wavenet is an AI Architectured based on a set of Convolutional Layers
   
    3. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using the following Composite 
       model configuration: 
   
        - A Composite Trade Strategy based on all the AI Trading Models configured above
   
        - The Set Entry cofiguration rules are: 
          a. Majority Voting (default)
          b. Enter a trade if the last n voting decisions are the same, set to 1 (default )
          c. Enter a trade if there is cross-over signaled after applying rules a) and b), 
             set to False (default)
   
        - The Set Exit configuration rules are: 
          a. Majority Voting (Default)
              - Exit a long trade if the percent of Trading AI Models signaled an exit for a long position
                 is greater that the percent signaling no-exit 
              - Exit a short trade if the percent of Trading AI Models signaled an exit for a short position
                 is greater that the percent signaling no-exit 
          b. Exit a trade if the last n exit decisions are the same, set to 1 (default )
   
   

   Backtest:

    1. Type: regular
   
    2. AI Models Fit Date Ranges
        1. Training Period: 
            1. Begin Date = 2002-01-01 
            2. End Date   = 2006-12-31  
        2. Validation Period: 
            1. Begin Date = 2007-01-01 
            2. End Date   = 2008-06-30 
   
    3. Test Period: 
        1. Begin Date = 2009-01-01 
        2. End Date   = 2009-12-31         

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 18:04:03,558 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

ts_model_pipline_mng_f = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_D",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

# Define Metadata for several Trading AI-Models for several Trade Strategies in the portfolio and one
# Trade Strategy based on a composite set of all Trading AI-Models. The composite trade strategy
# uses Majority Voting for entering and exiting a trade (default setting)

# The configuraiton data for each Trade Strategy is defined via a JSON/dict, and 
# they are added to the TradeStrategyConfigData() object
# 
# This configuration method is a short-cut when creating many trade strategies in a portfolio, or 
# a composite of many trade strategies. The JSON format follows the general format:
#
# *** Trade Strategy Config:
#
# 1. ts_unique_tag : unique tag to identify a trade strategy in a set
# 2. actions_conf : dict, 
#       Description: contains the configuration data for each action in a trade strategy, only
#                    the "entry_exit", sub-tag is required
#       Child tags:
#             "entry_exit" : required
#             "stop_lost"  : optional
#             "pos_sizing" : optional
#             "pyramiding" : optional
# 3. The format of each child-tag/action follows the same parameters as when defined via the class,
# 4. obj_args : custom set of parameters passed to the defined model defined via the model tag, this
#               is a dynamic set of parameters and specific to each model, some share common inputs.
#
#    Supported built-in models, for the entry_exit action:
#        [ "sma", "bb", "macd", "rsi", "srsi", "adx", "kama", "tsi",
#          "forceindex", "mfi", 'conv1d_gru', 'conv1d_lstm', 'wavenet', 
#          'wavenet_gru', 'wavenet_lstm', 'lstm', 'gru' ]
#
#    Custom models can be added via a custom function. The second strategy in the configuration
#    below ('ts_unique_tag' : "ts_gru_1") , defines the "custom_create_func" tag and sets equal to
#    "create_actions_ai_ts_model", this function should be already defined or imported. Thus for
#    trade strategy #2, the user custom function is used. Note, that for trade strategy: ts_gru_2
#    the model is also set to "gru", but in this case it is using the built-in gru model.
#    The actual implementantions for all supported models are included in this Jupyter Notebook, at
#    the beg. of this portfolio example, and a user can copy and altered as they see fit, it would
#    affect the built-in functions, which are scope in a pythong package.
#
#
# *** Trade Strategy Set Config:
#
# There is only one set configuration, and it is defined to apply policies on how to treat the 
# set actions, this configuration is optional, and defaults for each action are defined below
#
# "entry" : optional
#     Defines the policy to drive the trade decision to follow from the set. As explained in earlier 
#     examples. 
#     Default policy parameters are:
#           Majority Voting, 
#           incNoInvestInVotingDec = False, 
#           lastn_match = 1
#           cross_over_signal_for_trading
#
# "exit" : optional
#     Defines the policy to drive the exit decision to follow from the set. As explained in earlier 
#     examples. 
#     Default policy parameters are:
#           Majority Voting, 
#           lastn_match = 1
#

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_1", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )
# TS #3
    
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'   : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'  : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_wavenet", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "wavenet",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'      : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #6

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_wavenet_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "wavenet_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'      : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #7
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_wavenet_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "wavenet_lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'      : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #8
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_2", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'          : 40,   'no_neurons_lay1'    : 20,
                                         'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                         'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                         'epochs'          : 20 }  
                                }
                            } }  )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_f )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_f )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  27

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Multiple-AI-Trading Models TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio F, US-Indexes with Mult-AI-Trading and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 18:10:04,111 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio F, US-Indexes with Mult-AI-Trading and Long strategies" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

2021-10-28 18:11:01,158 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 18:11:01,159 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 18:11:01,159 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 18:11:01,160 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 18:11:01,161 [INFO] root: PortfolioRegisterItem init called. 
2021-10-28 18:11:01,162 [INFO] root: PortfolioRegisterItem init called. 
Portfolio of US-Indexes using SMA and Long strategies
Portfolio of US-Indexes using LSTM and Long strategies
Portfolio of US-Indexes with Multiple-SMA and Long strategies
Portfolio of US-Indexes with Multiple-SMA and Long strategies
Portfolio of US-Indexes with Multiple-LSTM and Long strategies
Portfolio of US-Indexes with Mult-AI-Trading and Long strategies
6 Portfolio of US-Indexes with Mult-AI-Trading and Long strategies

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2001-07-01",
                             strBegDate         = "2002-01-01",
                             strEndDate         = "2006-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2006-07-01",
                               strBegDate         = "2007-01-01",
                               strEndDate         = "2008-06-30" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2008-07-01",
                               strBegDate         = "2009-01-01",
                               strEndDate         = "2009-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio of Multiple-AI-Modeled based Trade Strategies",
                    timePeriod  = testing_period,
                    fitDatasets = fit_datasets,
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']]

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes with Mult-AI-Trading a...	Yes	845	60000000.0	7.009235e+07	0.168206	Completed
1	ts_gru_1_^DJI	No	72	2000000.0	2.410562e+06	0.205281	Completed
2	ts_lstm_^DJI	No	42	2000000.0	2.510186e+06	0.255093	Completed
3	ts_conv1d_gru_^DJI	No	50	2000000.0	2.232142e+06	0.116071	Completed
4	ts_conv1d_lstm_^DJI	No	34	2000000.0	2.922561e+06	0.461280	Completed
5	ts_wavenet_^DJI	No	40	2000000.0	2.267038e+06	0.133519	Completed
6	ts_wavenet_gru_^DJI	No	38	2000000.0	2.400650e+06	0.200325	Completed
7	ts_wavenet_lstm_^DJI	No	24	2000000.0	2.235235e+06	0.117617	Completed
8	ts_gru_2_^DJI	No	82	2000000.0	2.572858e+06	0.286429	Completed
9	all_^DJI	No	54	2000000.0	2.525422e+06	0.262711	Completed
10	ts_gru_1_^IXIC	No	64	2000000.0	2.351447e+06	0.175723	Completed
11	ts_lstm_^IXIC	No	40	2000000.0	1.834660e+06	-0.082670	Completed
12	ts_conv1d_gru_^IXIC	No	46	2000000.0	1.857777e+06	-0.071112	Completed
13	ts_conv1d_lstm_^IXIC	No	37	2000000.0	2.816539e+06	0.408269	Completed
14	ts_wavenet_^IXIC	No	2	2000000.0	1.915524e+06	-0.042238	Completed
15	ts_wavenet_gru_^IXIC	No	2	2000000.0	1.898595e+06	-0.050703	Completed
16	ts_wavenet_lstm_^IXIC	No	2	2000000.0	1.940465e+06	-0.029767	Completed
17	ts_gru_2_^IXIC	No	68	2000000.0	2.084553e+06	0.042277	Completed
18	all_^IXIC	No	48	2000000.0	2.167578e+06	0.083789	Completed
19	ts_gru_1_^GSPC	No	52	2000000.0	2.132592e+06	0.066296	Completed
20	ts_lstm_^GSPC	No	11	2000000.0	2.446656e+06	0.223328	Completed
21	ts_conv1d_gru_^GSPC	No	4	2000000.0	2.412789e+06	0.206395	Completed
22	ts_conv1d_lstm_^GSPC	No	3	2000000.0	2.419068e+06	0.209534	Completed
23	ts_wavenet_^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed
24	ts_wavenet_gru_^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed
25	ts_wavenet_lstm_^GSPC	No	3	2000000.0	2.541476e+06	0.270738	Completed
26	ts_gru_2_^GSPC	No	20	2000000.0	2.562216e+06	0.281108	Completed
27	all_^GSPC	No	2	2000000.0	2.365497e+06	0.182748	Completed
28	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed
29	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	2.780251e+06	0.390126	Completed
30	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	2.307932e+06	0.153966	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio of US-Indexes with Mult-AI-Trading a...	Yes			2009-01-05	2009-12-31	1.0	0.17	0.17	0.95	-0.17	0.06	-0.04
1	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2009-01-05	2009-12-31	1.0	0.21	0.21	0.89	-0.18	0.07	-0.04
2	ts_lstm_^DJI	No	ts_lstm	^DJI	2009-01-05	2009-12-31	1.0	0.26	0.26	1.06	-0.18	0.07	-0.04
3	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2009-01-05	2009-12-31	1.0	0.12	0.12	0.57	-0.2	0.07	-0.04
4	ts_conv1d_lstm_^DJI	No	ts_conv1d_lstm	^DJI	2009-01-05	2009-12-31	1.0	0.46	0.47	1.7	-0.17	0.07	-0.04
5	ts_wavenet_^DJI	No	ts_wavenet	^DJI	2009-01-05	2009-12-31	1.0	0.13	0.14	0.64	-0.21	0.07	-0.05
6	ts_wavenet_gru_^DJI	No	ts_wavenet_gru	^DJI	2009-01-05	2009-12-31	1.0	0.2	0.2	0.88	-0.17	0.07	-0.04
7	ts_wavenet_lstm_^DJI	No	ts_wavenet_lstm	^DJI	2009-01-05	2009-12-31	1.0	0.12	0.12	0.58	-0.17	0.07	-0.04
8	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2009-01-05	2009-12-31	1.0	0.29	0.29	1.16	-0.22	0.07	-0.04
9	all_^DJI	No	all	^DJI	2009-01-05	2009-12-31	0.97	0.26	0.27	1.1	-0.18	0.07	-0.04
10	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2009-01-05	2009-12-31	1.0	0.18	0.18	0.71	-0.25	0.07	-0.04
11	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2009-01-05	2009-12-31	1.0	-0.08	-0.08	-0.15	-0.28	0.07	-0.08
12	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2009-01-05	2009-12-31	1.0	-0.07	-0.07	-0.1	-0.3	0.07	-0.08
13	ts_conv1d_lstm_^IXIC	No	ts_conv1d_lstm	^IXIC	2009-01-05	2009-12-31	1.0	0.41	0.41	1.33	-0.21	0.07	-0.08
14	ts_wavenet_^IXIC	No	ts_wavenet	^IXIC	2009-01-05	2009-12-31	1.0	-0.04	-0.04	-0.0	-0.23	0.07	-0.06
15	ts_wavenet_gru_^IXIC	No	ts_wavenet_gru	^IXIC	2009-01-05	2009-12-31	1.0	-0.05	-0.05	-0.03	-0.23	0.07	-0.06
16	ts_wavenet_lstm_^IXIC	No	ts_wavenet_lstm	^IXIC	2009-01-05	2009-12-31	1.0	-0.03	-0.03	0.04	-0.23	0.07	-0.06
17	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2009-01-05	2009-12-31	1.0	0.04	0.04	0.29	-0.33	0.07	-0.04
18	all_^IXIC	No	all	^IXIC	2009-01-05	2009-12-31	0.97	0.08	0.09	0.43	-0.2	0.07	-0.06
19	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2009-01-05	2009-12-31	1.0	0.07	0.07	0.37	-0.19	0.07	-0.05
20	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2009-01-05	2009-12-31	1.0	0.22	0.23	0.88	-0.26	0.07	-0.05
21	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2009-01-05	2009-12-31	1.0	0.21	0.21	0.83	-0.26	0.07	-0.05
22	ts_conv1d_lstm_^GSPC	No	ts_conv1d_lstm	^GSPC	2009-01-05	2009-12-31	1.0	0.21	0.21	0.84	-0.28	0.07	-0.05
23	ts_wavenet_^GSPC	No	ts_wavenet	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
24	ts_wavenet_gru_^GSPC	No	ts_wavenet_gru	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
25	ts_wavenet_lstm_^GSPC	No	ts_wavenet_lstm	^GSPC	2009-01-05	2009-12-31	1.0	0.27	0.27	1.02	-0.26	0.07	-0.05
26	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2009-01-05	2009-12-31	1.0	0.28	0.29	1.05	-0.23	0.07	-0.05
27	all_^GSPC	No	all	^GSPC	2009-01-05	2009-12-31	0.99	0.18	0.19	0.76	-0.28	0.07	-0.05
28	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
29	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2009-01-05	2009-12-31	1.0	0.39	0.4	1.32	-0.23	0.07	-0.06
30	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2009-01-05	2009-12-31	1.0	0.15	0.16	0.72	-0.27	0.07	-0.05

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0	1	2	3	4	5	6	7	8	9	...	21	22	23	24	25	26	27	28	29	30
ta_reg_item_name	Portfolio of US-Indexes with Mult-AI-Trading a...	ts_gru_1_^DJI	ts_lstm_^DJI	ts_conv1d_gru_^DJI	ts_conv1d_lstm_^DJI	ts_wavenet_^DJI	ts_wavenet_gru_^DJI	ts_wavenet_lstm_^DJI	ts_gru_2_^DJI	all_^DJI	...	ts_conv1d_gru_^GSPC	ts_conv1d_lstm_^GSPC	ts_wavenet_^GSPC	ts_wavenet_gru_^GSPC	ts_wavenet_lstm_^GSPC	ts_gru_2_^GSPC	all_^GSPC	Long-Only Trade Strategy for ^GSPC	Long-Only Trade Strategy for ^IXIC	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	6	94	95	96	97	98	99	100	101	102	...	114	115	116	117	118	119	120	121	122	123
is_portfolio	Yes	No	No	No	No	No	No	No	No	No	...	No	No	No	No	No	No	No	No	No	No
model_type		ts_gru_1	ts_lstm	ts_conv1d_gru	ts_conv1d_lstm	ts_wavenet	ts_wavenet_gru	ts_wavenet_lstm	ts_gru_2	all	...	ts_conv1d_gru	ts_conv1d_lstm	ts_wavenet	ts_wavenet_gru	ts_wavenet_lstm	ts_gru_2	all	long	long	long
trade_asset		^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	...	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^IXIC	^DJI
test_period_len_days	364	364	364	364	364	364	364	364	364	364	...	364	364	364	364	364	364	364	364	364	364
tr_period_len_days	1825	1825	1825	1825	1825	1825	1825	1825	1825	1825	...	1825	1825	1825	1825	1825	1825	1825	1825	1825	1825
val_period_len_days	546	546	546	546	546	546	546	546	546	546	...	546	546	546	546	546	546	546	546	546	546
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	...	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	...	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05
End Period	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	...	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.97	...	1.0	1.0	1.0	1.0	1.0	1.0	0.99	1.0	1.0	1.0
Cumulative Return	0.17	0.21	0.26	0.12	0.46	0.13	0.2	0.12	0.29	0.26	...	0.21	0.21	0.2	0.2	0.27	0.28	0.18	0.2	0.39	0.15
CAGR%	0.17	0.21	0.26	0.12	0.47	0.14	0.2	0.12	0.29	0.27	...	0.21	0.21	0.2	0.2	0.27	0.29	0.19	0.2	0.4	0.16
Sharpe	0.95	0.89	1.06	0.57	1.7	0.64	0.88	0.58	1.16	1.1	...	0.83	0.84	0.8	0.8	1.02	1.05	0.76	0.8	1.32	0.72
Sortino	1.48	1.36	1.64	0.87	2.76	0.96	1.34	0.88	1.82	1.7	...	1.21	1.22	1.16	1.16	1.5	1.55	1.1	1.16	1.97	1.05
Sortino/√2	1.04	0.96	1.16	0.61	1.95	0.68	0.95	0.62	1.28	1.2	...	0.86	0.86	0.82	0.82	1.06	1.1	0.77	0.82	1.39	0.74
Max Drawdown	-0.17	-0.18	-0.18	-0.2	-0.17	-0.21	-0.17	-0.17	-0.22	-0.18	...	-0.26	-0.28	-0.28	-0.28	-0.26	-0.23	-0.28	-0.28	-0.23	-0.27
Longest DD Days	143	170	146	218	111	177	171	199	177	199	...	117	145	145	145	117	93	145	145	92	197
Volatility (ann.)	0.18	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.24	...	0.27	0.27	0.27	0.27	0.27	0.27	0.27	0.27	0.28	0.24
Calmar	1.01	1.14	1.48	0.59	2.68	0.65	1.16	0.68	1.33	1.52	...	0.82	0.77	0.72	0.72	1.07	1.22	0.67	0.72	1.71	0.57
Skew	0.74	0.34	0.35	0.38	0.42	0.29	0.36	0.38	0.42	0.36	...	0.07	0.03	0.04	0.04	0.04	0.06	0.05	0.04	0.09	0.17
Kurtosis	8.15	2.3	2.24	2.22	2.17	2.2	2.33	2.3	2.28	2.48	...	2.07	2.08	2.07	2.07	2.08	2.07	2.09	2.07	1.77	2.41
Expected Daily %	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Expected Monthly %	0.01	0.02	0.02	0.01	0.03	0.01	0.02	0.01	0.02	0.02	...	0.02	0.02	0.02	0.02	0.02	0.02	0.01	0.02	0.03	0.01
Expected Yearly %	0.17	0.21	0.26	0.12	0.46	0.13	0.2	0.12	0.29	0.26	...	0.21	0.21	0.2	0.2	0.27	0.28	0.18	0.2	0.39	0.15
Kelly Criterion	0.1	0.07	0.09	0.05	0.13	0.05	0.07	0.05	0.1	0.09	...	0.08	0.08	0.07	0.07	0.09	0.09	0.07	0.07	0.12	0.06
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	...	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.02
Expected Shortfall (cVaR)	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	...	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.02
Gain/Pain Ratio	0.24	0.17	0.2	0.1	0.34	0.12	0.17	0.11	0.23	0.22	...	0.16	0.16	0.15	0.15	0.2	0.2	0.14	0.15	0.26	0.13
Gain/Pain (1M)	1.34	1.4	2.16	0.63	5.79	0.61	1.38	0.89	1.66	1.82	...	1.18	0.93	0.88	0.88	1.5	1.46	0.83	0.88	1.91	0.71
Payoff Ratio	1.12	1.09	1.1	1.06	1.21	1.11	1.14	1.12	1.1	1.11	...	0.93	0.92	0.93	0.93	0.95	1.0	0.93	0.93	0.97	0.97
Profit Factor	1.24	1.17	1.2	1.1	1.34	1.12	1.17	1.11	1.23	1.22	...	1.16	1.16	1.15	1.15	1.2	1.2	1.14	1.15	1.26	1.13
Common Sense Ratio	1.34	1.15	1.34	1.19	1.75	1.14	1.23	1.23	1.27	1.38	...	1.17	1.08	1.07	1.07	1.21	1.22	1.06	1.07	1.33	1.15
CPC Index	0.73	0.66	0.69	0.6	0.86	0.62	0.67	0.61	0.71	0.71	...	0.6	0.59	0.59	0.59	0.63	0.66	0.58	0.59	0.69	0.59
Tail Ratio	1.08	0.99	1.11	1.08	1.3	1.02	1.06	1.11	1.04	1.14	...	1.01	0.93	0.93	0.93	1.01	1.01	0.93	0.93	1.06	1.01
Outlier Win Ratio	5.85	3.69	3.5	3.58	3.36	3.2	3.57	3.49	3.82	3.74	...	3.54	3.56	3.55	3.55	3.51	3.43	3.61	3.55	3.46	3.22
Outlier Loss Ratio	6.0	3.68	3.7	3.62	3.91	3.72	3.78	3.74	3.45	3.7	...	3.61	3.58	3.6	3.6	3.64	3.75	3.58	3.6	3.13	3.65
MTD	0.02	0.04	0.04	0.06	0.07	0.02	0.02	0.02	0.01	0.07	...	0.02	0.02	0.02	0.02	0.02	0.01	0.02	0.02	0.06	0.01
3M	0.03	0.08	0.1	0.11	0.09	0.09	0.03	-0.01	0.04	0.12	...	0.05	0.06	0.05	0.05	0.05	0.03	0.05	0.05	0.07	0.07
6M	0.03	-0.01	0.04	0.07	0.08	-0.05	-0.04	-0.07	-0.1	0.05	...	0.16	0.23	0.21	0.21	0.21	0.17	0.2	0.21	0.24	0.23
YTD	0.17	0.21	0.26	0.12	0.46	0.13	0.2	0.12	0.29	0.26	...	0.21	0.21	0.2	0.2	0.27	0.28	0.18	0.2	0.39	0.15
1Y	0.17	0.21	0.26	0.12	0.46	0.13	0.2	0.12	0.29	0.26	...	0.21	0.21	0.2	0.2	0.27	0.28	0.18	0.2	0.39	0.15
3Y (ann.)	0.17	0.21	0.26	0.12	0.47	0.14	0.2	0.12	0.29	0.27	...	0.21	0.21	0.2	0.2	0.27	0.29	0.19	0.2	0.4	0.16
5Y (ann.)	0.17	0.21	0.26	0.12	0.47	0.14	0.2	0.12	0.29	0.27	...	0.21	0.21	0.2	0.2	0.27	0.29	0.19	0.2	0.4	0.16
10Y (ann.)	0.17	0.21	0.26	0.12	0.47	0.14	0.2	0.12	0.29	0.27	...	0.21	0.21	0.2	0.2	0.27	0.29	0.19	0.2	0.4	0.16
All-time (ann.)	0.17	0.21	0.26	0.12	0.47	0.14	0.2	0.12	0.29	0.27	...	0.21	0.21	0.2	0.2	0.27	0.29	0.19	0.2	0.4	0.16
Best Day	0.06	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	...	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07
Worst Day	-0.04	-0.04	-0.04	-0.04	-0.04	-0.05	-0.04	-0.04	-0.04	-0.04	...	-0.05	-0.05	-0.05	-0.05	-0.05	-0.05	-0.05	-0.05	-0.06	-0.05
Best Month	0.12	0.25	0.12	0.22	0.15	0.08	0.13	0.08	0.17	0.12	...	0.09	0.09	0.09	0.09	0.09	0.13	0.09	0.09	0.12	0.09
Worst Month	-0.07	-0.06	-0.07	-0.08	-0.05	-0.12	-0.08	-0.11	-0.09	-0.08	...	-0.11	-0.11	-0.11	-0.11	-0.11	-0.11	-0.12	-0.11	-0.1	-0.12
Best Year	0.17	0.21	0.26	0.12	0.46	0.13	0.2	0.12	0.29	0.26	...	0.21	0.21	0.2	0.2	0.27	0.28	0.18	0.2	0.39	0.15
Worst Year	0.17	0.21	0.26	0.12	0.46	0.13	0.2	0.12	0.29	0.26	...	0.21	0.21	0.2	0.2	0.27	0.28	0.18	0.2	0.39	0.15
Avg. Drawdown	-0.02	-0.05	-0.04	-0.09	-0.03	-0.06	-0.04	-0.04	-0.05	-0.05	...	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03
Avg. Drawdown Days	16	28	24	59	16	33	18	22	22	24	...	14	15	15	15	13	14	15	15	12	15
Recovery Factor	0.99	1.12	1.45	0.58	2.64	0.64	1.15	0.67	1.31	1.5	...	0.81	0.76	0.71	0.71	1.06	1.2	0.66	0.71	1.68	0.56
Ulcer Index	1.2	1.04	1.04	1.05	1.03	1.04	1.04	1.04	1.03	1.04	...	1.06	1.26	1.26	1.26	1.05	1.43	inf	1.26	1.11	1.21
Avg. Up Month	0.04	0.06	0.05	0.05	0.05	0.04	0.04	0.04	0.08	0.05	...	0.04	0.05	0.05	0.05	0.05	0.06	0.05	0.05	0.06	0.05

60 rows × 31 columns

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

 done

Portfolio G

1. Portfolio G: Combines all Trade Strategies in Portfolio E and F

   Description:

    1. Portfolio with 63 trading strategies
    2. $200,000/strategy, Total Budget = $12.6M
    3. No-Rebalance during the backtest 
    4. 100% capital invested
    5. Portfolio investment using equal weights for each trade strategy
   
   

   Trade Strategies:

    1. Three trade strategies: Nasdaq, S&P 500, and DOW Jones with buy-and-hold
   
    2. Fifty-Seven trade strategies, 19 trade strategies per US Index, as defined in
       Portfolio E and F
   
    3. Three trade strategies: Nasdaq, S&P 500, and DOW Jones using the following Composite 
       model configuration: 
   
        - A Composite Trade Strategy based on all the Trading Models configured above per US Index
   
        - The Set Entry cofiguration rules are: 
          a. Majority Voting (default)
          b. Enter a trade if the last n voting decisions are the same, set to 1 (default )
          c. Enter a trade if there is cross-over signaled after applying rules a) and b), 
             set to False (default)
   
        - The Set Exit configuration rules are: 
          a. Majority Voting (Default)
              - Exit a long trade if the percent of Trading Models signaled an exit for a long position
                 is greater that the percent signaling no-exit 
              - Exit a short trade if the percent of Trading Models signaled an exit for a short position
                 is greater that the percent signaling no-exit 
          b. Exit a trade if the last n exit decisions are the same, set to 1 (default )

Backtest:

1. Type: regular

2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2006-12-31  
    2. Validation Period: 
        1. Begin Date = 2007-01-01 
        2. End Date   = 2008-12-31 

3. Test Period: 
    1. Begin Date = 2009-01-01 
    2. End Date   = 2009-12-31         

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 19:05:17,293 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

ts_model_pipline_mng_g = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_G",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_1", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )
# TS #3
    
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'   : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'  : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_wavenet", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "wavenet",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'      : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #6

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_wavenet_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "wavenet_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'      : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #7
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_wavenet_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "wavenet_lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'      : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #8
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_2", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'          : 40,   'no_neurons_lay1'    : 20,
                                         'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                         'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                         'epochs'          : 20 }  
                                }
                            } }  )

# TS #9

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_1",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #10

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #11

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_bb",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "bb",
                                  'type'     : "ta",
                                  'obj_args' : { 'window' : 13, 'window_dev' : 1 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #12

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_macd",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "macd",
                                  'type'     : "ta",
                                  'obj_args' : { 'window_slow' : 26, 'window_fast' : 12, 'window_sign' : 9,
                                                 'enter_long_macd_diff'  : 0.5 , 'exi_long_macd_diff' : 0.5,
                                                 'enter_short_macd_diff' : -0.5, 'exi_short_macd_diff' : -0.5  },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #13

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_rsi",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "rsi",
                                  'type'     : "ta",
                                  'obj_args' : { 'rsi_enter_long'  : 30.0,  'rsi_exit_long'  : 30.0, 
                                                 'rsi_enter_short' : 70.0,  'rsi_exit_short' : 70.0 },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #14

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_srsi",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "srsi",
                                  'type'     : "ta",
                                  'obj_args' : { 'window' : 14,  'smooth1' : 3, 'smooth2'  : 3,
                                                 'srsi_enter_long'  : 0.8,  'srsi_exit_long'  : 0.8,
                                                 'srsi_enter_short' : 0.2,  'srsi_exit_short' : 0.2 },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #15

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_adx",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "adx",
                              'type'     : "ta",
                              'obj_args' : { 'window' : 14, 'adx_enter_long_ge' : 14, 'adx_exit_long_lt'  : 14,
                                             'adx_enter_short_ge' : 14, 'adx_exit_short_lt' : 14 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #16

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_kama",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "kama",
                              'type'     : "ta",
                              'obj_args' : { 'window_fast' : 10, 'pow1_fast' : 2, 'pow2_fast'  : 30,
                                             'window_slow' : 10, 'pow1_slow' : 5, 'pow2_slow'  : 30 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #17

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_tsi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "tsi",
                              'type'     : "ta",
                              'obj_args' : { 'window_slow' : 25, 'window_fast' : 5 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

# TS #118

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_forceindex",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "forceindex",
                              'type'     : "ta",
                              'obj_args' : { 'window' : 39 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

# TS #19

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_mfi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "mfi",
                              'type'     : "ta",
                              'obj_args' : { 'window' : 14, 'long_slope_win' : 15, 'short_slope_win' : 15 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_g )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_g )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  60

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - TechAnal & AI Trading Models TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio G, US-Indexes with TechAnal & AI Trading Models, and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 19:06:34,488 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio G, US-Indexes with TechAnal & AI Trading Models, and Long strategies" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2000-07-01",
                             strBegDate         = "2001-01-01",
                             strEndDate         = "2006-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2006-07-01",
                               strBegDate         = "2007-01-01",
                               strEndDate         = "2008-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2008-07-01",
                               strBegDate         = "2009-01-01",
                               strEndDate         = "2009-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio G",
                    timePeriod  = testing_period,
                    fitDatasets = fit_datasets,
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Trade Strategies Execution & Investment Metrics

DOW Jones

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("DJI")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
1	ts_gru_1_^DJI	No	74	2000000.0	2.522902e+06	0.261451	Completed
2	ts_lstm_^DJI	No	42	2000000.0	2.490160e+06	0.245080	Completed
3	ts_conv1d_gru_^DJI	No	54	2000000.0	2.246321e+06	0.123160	Completed
4	ts_conv1d_lstm_^DJI	No	36	2000000.0	2.384650e+06	0.192325	Completed
5	ts_wavenet_^DJI	No	39	2000000.0	2.317959e+06	0.158980	Completed
6	ts_wavenet_gru_^DJI	No	49	2000000.0	2.489064e+06	0.244532	Completed
7	ts_wavenet_lstm_^DJI	No	41	2000000.0	2.545570e+06	0.272785	Completed
8	ts_gru_2_^DJI	No	86	2000000.0	2.409593e+06	0.204797	Completed
9	ts_sma_1_^DJI	No	6	2000000.0	2.368059e+06	0.184029	Completed
10	ts_sma_2_^DJI	No	4	2000000.0	2.454280e+06	0.227140	Completed
11	ts_bb_^DJI	No	46	2000000.0	1.880761e+06	-0.059619	Completed
12	ts_macd_^DJI	No	21	2000000.0	1.808427e+06	-0.095786	Completed
13	ts_rsi_^DJI	No	11	2000000.0	2.163856e+06	0.081928	Completed
14	ts_srsi_^DJI	No	39	2000000.0	2.060352e+06	0.030176	Completed
15	ts_adx_^DJI	No	22	2000000.0	2.145482e+06	0.072741	Completed
16	ts_kama_^DJI	No	3	2000000.0	2.653478e+06	0.326739	Completed
17	ts_tsi_^DJI	No	7	2000000.0	2.550770e+06	0.275385	Completed
18	ts_forceindex_^DJI	No	14	2000000.0	2.318384e+06	0.159192	Completed
19	ts_mfi_^DJI	No	32	2000000.0	1.724867e+06	-0.137567	Completed
20	all_^DJI	No	76	2000000.0	2.389533e+06	0.194766	Completed
63	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	2.307932e+06	0.153966	Completed

Nasdaq

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("IXIC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
21	ts_gru_1_^IXIC	No	74	2000000.0	2.527445e+06	0.263723	Completed
22	ts_lstm_^IXIC	No	44	2000000.0	2.277402e+06	0.138701	Completed
23	ts_conv1d_gru_^IXIC	No	50	2000000.0	2.358985e+06	0.179492	Completed
24	ts_conv1d_lstm_^IXIC	No	36	2000000.0	2.212704e+06	0.106352	Completed
25	ts_wavenet_^IXIC	No	2	2000000.0	1.898595e+06	-0.050703	Completed
26	ts_wavenet_gru_^IXIC	No	2	2000000.0	1.940465e+06	-0.029767	Completed
27	ts_wavenet_lstm_^IXIC	No	2	2000000.0	1.898595e+06	-0.050703	Completed
28	ts_gru_2_^IXIC	No	72	2000000.0	2.806475e+06	0.403238	Completed
29	ts_sma_1_^IXIC	No	7	2000000.0	2.125976e+06	0.062988	Completed
30	ts_sma_2_^IXIC	No	6	2000000.0	2.530324e+06	0.265162	Completed
31	ts_bb_^IXIC	No	40	2000000.0	2.086084e+06	0.043042	Completed
32	ts_macd_^IXIC	No	30	2000000.0	1.772608e+06	-0.113696	Completed
33	ts_rsi_^IXIC	No	4	2000000.0	1.823801e+06	-0.088100	Completed
34	ts_srsi_^IXIC	No	46	2000000.0	1.845952e+06	-0.077024	Completed
35	ts_adx_^IXIC	No	18	2000000.0	2.099621e+06	0.049811	Completed
36	ts_kama_^IXIC	No	3	2000000.0	2.621990e+06	0.310995	Completed
37	ts_tsi_^IXIC	No	10	2000000.0	1.897423e+06	-0.051289	Completed
38	ts_forceindex_^IXIC	No	15	2000000.0	2.131114e+06	0.065557	Completed
39	ts_mfi_^IXIC	No	36	2000000.0	1.808008e+06	-0.095996	Completed
40	all_^IXIC	No	80	2000000.0	1.992194e+06	-0.003903	Completed
62	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	2.780251e+06	0.390126	Completed

S&P 500

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("GSPC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
41	ts_gru_1_^GSPC	No	22	2000000.0	2.695396e+06	0.347698	Completed
42	ts_lstm_^GSPC	No	4	2000000.0	2.410210e+06	0.205105	Completed
43	ts_conv1d_gru_^GSPC	No	2	2000000.0	2.526111e+06	0.263055	Completed
44	ts_conv1d_lstm_^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed
45	ts_wavenet_^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed
46	ts_wavenet_gru_^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed
47	ts_wavenet_lstm_^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed
48	ts_gru_2_^GSPC	No	32	2000000.0	2.621147e+06	0.310574	Completed
49	ts_sma_1_^GSPC	No	7	2000000.0	2.129982e+06	0.064991	Completed
50	ts_sma_2_^GSPC	No	7	2000000.0	2.504244e+06	0.252122	Completed
51	ts_bb_^GSPC	No	36	2000000.0	2.091174e+06	0.045587	Completed
52	ts_macd_^GSPC	No	34	2000000.0	1.572109e+06	-0.213945	Completed
53	ts_rsi_^GSPC	No	12	2000000.0	2.203553e+06	0.101777	Completed
54	ts_srsi_^GSPC	No	41	2000000.0	1.769603e+06	-0.115198	Completed
55	ts_adx_^GSPC	No	23	2000000.0	2.162504e+06	0.081252	Completed
56	ts_kama_^GSPC	No	2	2000000.0	2.532568e+06	0.266284	Completed
57	ts_tsi_^GSPC	No	10	2000000.0	2.153106e+06	0.076553	Completed
58	ts_forceindex_^GSPC	No	15	2000000.0	2.053591e+06	0.026795	Completed
59	ts_mfi_^GSPC	No	35	2000000.0	1.776214e+06	-0.111893	Completed
60	all_^GSPC	No	27	2000000.0	2.639818e+06	0.319909	Completed
61	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	2.393362e+06	0.196681	Completed

Performance Metrics

Dow Jones

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^DJI" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
1	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2009-01-05	2009-12-31	1.0	0.26	0.27	1.09	-0.2	0.07	-0.04
2	ts_lstm_^DJI	No	ts_lstm	^DJI	2009-01-05	2009-12-31	1.0	0.25	0.25	1.03	-0.18	0.07	-0.04
3	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2009-01-05	2009-12-31	1.0	0.12	0.12	0.6	-0.2	0.07	-0.04
4	ts_conv1d_lstm_^DJI	No	ts_conv1d_lstm	^DJI	2009-01-05	2009-12-31	1.0	0.19	0.2	0.85	-0.18	0.06	-0.07
5	ts_wavenet_^DJI	No	ts_wavenet	^DJI	2009-01-05	2009-12-31	1.0	0.16	0.16	0.73	-0.22	0.07	-0.04
6	ts_wavenet_gru_^DJI	No	ts_wavenet_gru	^DJI	2009-01-05	2009-12-31	1.0	0.24	0.25	1.03	-0.21	0.07	-0.05
7	ts_wavenet_lstm_^DJI	No	ts_wavenet_lstm	^DJI	2009-01-05	2009-12-31	1.0	0.27	0.28	1.12	-0.21	0.07	-0.05
8	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2009-01-05	2009-12-31	1.0	0.2	0.21	0.89	-0.19	0.07	-0.04
9	ts_sma_1_^DJI	No	ts_sma_1	^DJI	2009-01-05	2009-12-31	0.99	0.18	0.19	0.87	-0.15	0.05	-0.06
10	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2009-01-05	2009-12-31	0.99	0.23	0.23	1.11	-0.17	0.04	-0.05
11	ts_bb_^DJI	No	ts_bb	^DJI	2009-01-05	2009-12-31	0.71	-0.06	-0.06	-0.18	-0.22	0.06	-0.07
12	ts_macd_^DJI	No	ts_macd	^DJI	2009-01-05	2009-12-31	0.93	-0.1	-0.1	-0.34	-0.23	0.07	-0.05
13	ts_rsi_^DJI	No	ts_rsi	^DJI	2009-01-05	2009-12-31	0.09	0.08	0.08	0.96	-0.03	0.06	-0.03
14	ts_srsi_^DJI	No	ts_srsi	^DJI	2009-01-05	2009-12-31	0.47	0.03	0.03	0.26	-0.15	0.07	-0.05
15	ts_adx_^DJI	No	ts_adx	^DJI	2009-01-05	2009-12-31	0.89	0.07	0.07	0.46	-0.21	0.04	-0.04
16	ts_kama_^DJI	No	ts_kama	^DJI	2009-01-05	2009-12-31	1.0	0.33	0.33	1.52	-0.19	0.04	-0.05
17	ts_tsi_^DJI	No	ts_tsi	^DJI	2009-01-05	2009-12-31	0.98	0.28	0.28	1.21	-0.11	0.07	-0.04
18	ts_forceindex_^DJI	No	ts_forceindex	^DJI	2009-01-05	2009-12-31	0.94	0.16	0.16	0.8	-0.13	0.04	-0.05
19	ts_mfi_^DJI	No	ts_mfi	^DJI	2009-01-05	2009-12-31	0.54	-0.14	-0.14	-0.8	-0.27	0.04	-0.04
20	all_^DJI	No	all	^DJI	2009-01-05	2009-12-31	0.95	0.19	0.2	0.88	-0.12	0.07	-0.04
63	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2009-01-05	2009-12-31	1.0	0.15	0.16	0.72	-0.27	0.07	-0.05

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^DJI" ').T.head(60)

	1	2	3	4	5	6	7	8	9	10	...	12	13	14	15	16	17	18	19	20	63
ta_reg_item_name	ts_gru_1_^DJI	ts_lstm_^DJI	ts_conv1d_gru_^DJI	ts_conv1d_lstm_^DJI	ts_wavenet_^DJI	ts_wavenet_gru_^DJI	ts_wavenet_lstm_^DJI	ts_gru_2_^DJI	ts_sma_1_^DJI	ts_sma_2_^DJI	...	ts_macd_^DJI	ts_rsi_^DJI	ts_srsi_^DJI	ts_adx_^DJI	ts_kama_^DJI	ts_tsi_^DJI	ts_forceindex_^DJI	ts_mfi_^DJI	all_^DJI	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	None	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
is_portfolio	No	No	No	No	No	No	No	No	No	No	...	No	No	No	No	No	No	No	No	No	No
model_type	ts_gru_1	ts_lstm	ts_conv1d_gru	ts_conv1d_lstm	ts_wavenet	ts_wavenet_gru	ts_wavenet_lstm	ts_gru_2	ts_sma_1	ts_sma_2	...	ts_macd	ts_rsi	ts_srsi	ts_adx	ts_kama	ts_tsi	ts_forceindex	ts_mfi	all	long
trade_asset	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	...	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI	^DJI
test_period_len_days	364	364	364	364	364	364	364	364	364	364	...	364	364	364	364	364	364	364	364	364	364
tr_period_len_days	2190	2190	2190	2190	2190	2190	2190	2190	2190	2190	...	2190	2190	2190	2190	2190	2190	2190	2190	2190	2190
val_period_len_days	730	730	730	730	730	730	730	730	730	730	...	730	730	730	730	730	730	730	730	730	730
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	...	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	...	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05
End Period	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	...	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.99	0.99	...	0.93	0.09	0.47	0.89	1.0	0.98	0.94	0.54	0.95	1.0
Cumulative Return	0.26	0.25	0.12	0.19	0.16	0.24	0.27	0.2	0.18	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.19	0.15
CAGR%	0.27	0.25	0.12	0.2	0.16	0.25	0.28	0.21	0.19	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.2	0.16
Sharpe	1.09	1.03	0.6	0.85	0.73	1.03	1.12	0.89	0.87	1.11	...	-0.34	0.96	0.26	0.46	1.52	1.21	0.8	-0.8	0.88	0.72
Sortino	1.67	1.6	0.9	1.23	1.15	1.57	1.71	1.36	1.25	1.62	...	-0.5	2.06	0.39	0.65	2.29	1.87	1.15	-1.09	1.4	1.05
Sortino/√2	1.18	1.13	0.64	0.87	0.82	1.11	1.21	0.96	0.88	1.15	...	-0.35	1.45	0.28	0.46	1.62	1.32	0.81	-0.77	0.99	0.74
Max Drawdown	-0.2	-0.18	-0.2	-0.18	-0.22	-0.21	-0.21	-0.19	-0.15	-0.17	...	-0.23	-0.03	-0.15	-0.21	-0.19	-0.11	-0.13	-0.27	-0.12	-0.27
Longest DD Days	182	210	213	169	177	177	147	177	147	296	...	255	150	279	296	219	92	99	294	177	197
Volatility (ann.)	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.24	0.22	0.2	...	0.22	0.09	0.17	0.2	0.2	0.22	0.21	0.17	0.23	0.24
Calmar	1.36	1.42	0.62	1.11	0.72	1.19	1.33	1.1	1.27	1.38	...	-0.42	2.5	0.2	0.35	1.78	2.64	1.23	-0.52	1.66	0.57
Skew	0.37	0.4	0.37	-0.25	0.6	0.29	0.23	0.38	-0.26	-0.19	...	0.38	5.64	0.78	-0.02	-0.1	0.41	-0.19	0.07	0.64	0.17
Kurtosis	2.33	2.27	2.24	2.21	2.19	2.25	2.35	2.34	1.67	0.9	...	2.68	67.83	10.28	1.09	0.81	2.13	1.04	3.07	2.92	2.41
Expected Daily %	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	-0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.0	0.0	0.0
Expected Monthly %	0.02	0.02	0.01	0.01	0.01	0.02	0.02	0.02	0.01	0.02	...	-0.01	0.01	0.0	0.01	0.02	0.02	0.01	-0.01	0.01	0.01
Expected Yearly %	0.26	0.25	0.12	0.19	0.16	0.24	0.27	0.2	0.18	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.19	0.15
Kelly Criterion	0.09	0.08	0.05	0.07	0.06	0.08	0.09	0.08	0.08	0.09	...	-0.03	0.28	0.03	0.04	0.13	0.1	0.07	-0.09	0.07	0.06
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	...	-0.02	-0.01	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02
Expected Shortfall (cVaR)	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	...	-0.02	-0.01	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02	-0.02
Gain/Pain Ratio	0.21	0.2	0.11	0.16	0.14	0.2	0.22	0.17	0.16	0.21	...	-0.06	0.97	0.07	0.09	0.29	0.23	0.15	-0.16	0.17	0.13
Gain/Pain (1M)	1.22	1.15	0.55	1.7	0.86	0.84	1.28	1.48	1.13	1.36	...	-0.32	2.78	0.35	0.41	2.62	5.12	1.28	-0.37	2.58	0.71
Payoff Ratio	1.09	1.19	1.1	1.1	1.16	1.13	1.13	1.07	1.0	0.99	...	1.08	1.48	1.15	0.92	1.01	1.07	0.96	0.96	1.21	0.97
Profit Factor	1.21	1.2	1.11	1.16	1.14	1.2	1.22	1.17	1.16	1.21	...	0.94	1.97	1.07	1.09	1.29	1.23	1.15	0.84	1.17	1.13
Common Sense Ratio	1.26	1.34	1.19	1.29	1.41	1.39	1.33	1.17	1.11	1.33	...	0.98	NaN	0.95	1.16	1.44	1.44	1.28	0.63	1.38	1.15
CPC Index	0.69	0.71	0.61	0.65	0.65	0.7	0.71	0.65	0.63	0.66	...	0.47	1.67	0.59	0.54	0.73	0.71	0.6	0.37	0.7	0.59
Tail Ratio	1.04	1.12	1.07	1.11	1.24	1.16	1.09	1.0	0.96	1.1	...	1.05	NaN	0.89	1.07	1.12	1.17	1.11	0.76	1.18	1.01
Outlier Win Ratio	3.87	3.42	3.51	3.0	3.65	3.2	3.22	3.89	3.24	3.19	...	3.77	23.99	10.39	4.09	3.12	3.44	3.61	8.17	4.31	3.22
Outlier Loss Ratio	3.49	3.87	3.69	3.83	3.38	3.79	3.83	3.46	3.39	3.35	...	3.32	1.3	3.03	3.03	2.83	3.15	3.22	2.54	3.49	3.65
MTD	0.04	0.04	0.02	0.04	0.02	0.04	0.04	-0.0	0.01	0.01	...	-0.02	0.0	-0.03	-0.04	0.01	0.01	0.01	-0.03	0.02	0.01
3M	0.09	0.01	0.08	0.06	-0.05	0.07	0.08	0.02	0.07	0.07	...	-0.06	0.0	-0.02	-0.03	0.07	0.02	-0.02	-0.11	0.04	0.07
6M	-0.05	0.02	-0.03	0.01	-0.18	-0.09	0.04	-0.08	0.07	0.01	...	-0.12	-0.0	-0.03	0.06	0.23	0.1	0.12	-0.12	-0.02	0.23
YTD	0.26	0.25	0.12	0.19	0.16	0.24	0.27	0.2	0.18	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.19	0.15
1Y	0.26	0.25	0.12	0.19	0.16	0.24	0.27	0.2	0.18	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.19	0.15
3Y (ann.)	0.27	0.25	0.12	0.2	0.16	0.25	0.28	0.21	0.19	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.2	0.16
5Y (ann.)	0.27	0.25	0.12	0.2	0.16	0.25	0.28	0.21	0.19	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.2	0.16
10Y (ann.)	0.27	0.25	0.12	0.2	0.16	0.25	0.28	0.21	0.19	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.2	0.16
All-time (ann.)	0.27	0.25	0.12	0.2	0.16	0.25	0.28	0.21	0.19	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.2	0.16
Best Day	0.07	0.07	0.07	0.06	0.07	0.07	0.07	0.07	0.05	0.04	...	0.07	0.06	0.07	0.04	0.04	0.07	0.04	0.04	0.07	0.07
Worst Day	-0.04	-0.04	-0.04	-0.07	-0.04	-0.05	-0.05	-0.04	-0.06	-0.05	...	-0.05	-0.03	-0.05	-0.04	-0.05	-0.04	-0.05	-0.04	-0.04	-0.05
Best Month	0.15	0.15	0.26	0.1	0.12	0.2	0.09	0.1	0.12	0.11	...	0.08	0.11	0.06	0.1	0.11	0.11	0.07	0.09	0.09	0.09
Worst Month	-0.1	-0.08	-0.11	-0.07	-0.11	-0.11	-0.11	-0.05	-0.06	-0.11	...	-0.05	-0.02	-0.06	-0.07	-0.05	-0.03	-0.07	-0.08	-0.05	-0.12
Best Year	0.26	0.25	0.12	0.19	0.16	0.24	0.27	0.2	0.18	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.19	0.15
Worst Year	0.26	0.25	0.12	0.19	0.16	0.24	0.27	0.2	0.18	0.23	...	-0.1	0.08	0.03	0.07	0.33	0.28	0.16	-0.14	0.19	0.15
Avg. Drawdown	-0.05	-0.03	-0.05	-0.06	-0.04	-0.04	-0.05	-0.05	-0.03	-0.03	...	-0.05	-0.02	-0.04	-0.04	-0.02	-0.02	-0.03	-0.06	-0.04	-0.03
Avg. Drawdown Days	30	31	25	38	15	21	23	33	18	33	...	30	76	43	34	16	15	17	55	17	15
Recovery Factor	1.34	1.39	0.61	1.1	0.71	1.18	1.3	1.08	1.25	1.36	...	-0.42	2.47	0.2	0.34	1.75	2.6	1.21	-0.51	1.63	0.56
Ulcer Index	1.03	1.04	1.04	1.04	1.03	1.03	1.04	1.03	1.13	1.04	...	1.11	inf	1.01	1.07	1.03	1.09	1.1	1.05	1.03	1.21
Avg. Up Month	0.08	0.06	0.06	0.04	0.05	0.08	0.05	0.07	0.05	0.04	...	0.04	0.04	0.02	0.04	0.05	0.04	0.03	0.06	0.05	0.05

60 rows × 21 columns

Nasdaq

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^IXIC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
21	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2009-01-05	2009-12-31	1.0	0.26	0.27	0.96	-0.21	0.07	-0.04
22	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2009-01-05	2009-12-31	1.0	0.14	0.14	0.6	-0.23	0.07	-0.06
23	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2009-01-05	2009-12-31	1.0	0.18	0.18	0.72	-0.21	0.07	-0.06
24	ts_conv1d_lstm_^IXIC	No	ts_conv1d_lstm	^IXIC	2009-01-05	2009-12-31	1.0	0.11	0.11	0.49	-0.25	0.07	-0.08
25	ts_wavenet_^IXIC	No	ts_wavenet	^IXIC	2009-01-05	2009-12-31	1.0	-0.05	-0.05	-0.03	-0.23	0.07	-0.06
26	ts_wavenet_gru_^IXIC	No	ts_wavenet_gru	^IXIC	2009-01-05	2009-12-31	1.0	-0.03	-0.03	0.04	-0.23	0.07	-0.06
27	ts_wavenet_lstm_^IXIC	No	ts_wavenet_lstm	^IXIC	2009-01-05	2009-12-31	1.0	-0.05	-0.05	-0.03	-0.23	0.07	-0.06
28	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2009-01-05	2009-12-31	1.0	0.4	0.41	1.33	-0.25	0.07	-0.04
29	ts_sma_1_^IXIC	No	ts_sma_1	^IXIC	2009-01-05	2009-12-31	0.98	0.06	0.06	0.36	-0.23	0.05	-0.07
30	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2009-01-05	2009-12-31	0.99	0.27	0.27	0.99	-0.21	0.05	-0.07
31	ts_bb_^IXIC	No	ts_bb	^IXIC	2009-01-05	2009-12-31	0.67	0.04	0.04	0.3	-0.17	0.07	-0.07
32	ts_macd_^IXIC	No	ts_macd	^IXIC	2009-01-05	2009-12-31	0.89	-0.11	-0.12	-0.33	-0.15	0.07	-0.05
33	ts_rsi_^IXIC	No	ts_rsi	^IXIC	2009-01-05	2009-12-31	0.07	-0.09	-0.09	-1.19	-0.1	0.01	-0.07
34	ts_srsi_^IXIC	No	ts_srsi	^IXIC	2009-01-05	2009-12-31	0.54	-0.08	-0.08	-0.25	-0.22	0.07	-0.07
35	ts_adx_^IXIC	No	ts_adx	^IXIC	2009-01-05	2009-12-31	0.9	0.05	0.05	0.32	-0.14	0.07	-0.05
36	ts_kama_^IXIC	No	ts_kama	^IXIC	2009-01-05	2009-12-31	1.0	0.31	0.32	1.2	-0.21	0.05	-0.05
37	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2009-01-05	2009-12-31	0.97	-0.05	-0.05	-0.06	-0.23	0.07	-0.05
38	ts_forceindex_^IXIC	No	ts_forceindex	^IXIC	2009-01-05	2009-12-31	0.94	0.07	0.07	0.37	-0.21	0.07	-0.05
39	ts_mfi_^IXIC	No	ts_mfi	^IXIC	2009-01-05	2009-12-31	0.52	-0.1	-0.1	-0.4	-0.16	0.06	-0.07
40	all_^IXIC	No	all	^IXIC	2009-01-05	2009-12-31	0.96	-0.0	-0.0	0.13	-0.22	0.07	-0.07
62	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2009-01-05	2009-12-31	1.0	0.39	0.4	1.32	-0.23	0.07	-0.06

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^IXIC" ').T.head(60)

	21	22	23	24	25	26	27	28	29	30	...	32	33	34	35	36	37	38	39	40	62
ta_reg_item_name	ts_gru_1_^IXIC	ts_lstm_^IXIC	ts_conv1d_gru_^IXIC	ts_conv1d_lstm_^IXIC	ts_wavenet_^IXIC	ts_wavenet_gru_^IXIC	ts_wavenet_lstm_^IXIC	ts_gru_2_^IXIC	ts_sma_1_^IXIC	ts_sma_2_^IXIC	...	ts_macd_^IXIC	ts_rsi_^IXIC	ts_srsi_^IXIC	ts_adx_^IXIC	ts_kama_^IXIC	ts_tsi_^IXIC	ts_forceindex_^IXIC	ts_mfi_^IXIC	all_^IXIC	Long-Only Trade Strategy for ^IXIC
ta_reg_item_id	None	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
is_portfolio	No	No	No	No	No	No	No	No	No	No	...	No	No	No	No	No	No	No	No	No	No
model_type	ts_gru_1	ts_lstm	ts_conv1d_gru	ts_conv1d_lstm	ts_wavenet	ts_wavenet_gru	ts_wavenet_lstm	ts_gru_2	ts_sma_1	ts_sma_2	...	ts_macd	ts_rsi	ts_srsi	ts_adx	ts_kama	ts_tsi	ts_forceindex	ts_mfi	all	long
trade_asset	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	...	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC	^IXIC
test_period_len_days	364	364	364	364	364	364	364	364	364	364	...	364	364	364	364	364	364	364	364	364	364
tr_period_len_days	2190	2190	2190	2190	2190	2190	2190	2190	2190	2190	...	2190	2190	2190	2190	2190	2190	2190	2190	2190	2190
val_period_len_days	730	730	730	730	730	730	730	730	730	730	...	730	730	730	730	730	730	730	730	730	730
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	...	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	...	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05
End Period	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	...	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.98	0.99	...	0.89	0.07	0.54	0.9	1.0	0.97	0.94	0.52	0.96	1.0
Cumulative Return	0.26	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.4	0.06	0.27	...	-0.11	-0.09	-0.08	0.05	0.31	-0.05	0.07	-0.1	-0.0	0.39
CAGR%	0.27	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.41	0.06	0.27	...	-0.12	-0.09	-0.08	0.05	0.32	-0.05	0.07	-0.1	-0.0	0.4
Sharpe	0.96	0.6	0.72	0.49	-0.03	0.04	-0.03	1.33	0.36	0.99	...	-0.33	-1.19	-0.25	0.32	1.2	-0.06	0.37	-0.4	0.13	1.32
Sortino	1.52	0.89	1.07	0.71	-0.05	0.06	-0.05	2.15	0.49	1.37	...	-0.47	-1.21	-0.37	0.46	1.76	-0.08	0.54	-0.54	0.18	1.97
Sortino/√2	1.08	0.63	0.76	0.5	-0.03	0.04	-0.03	1.52	0.35	0.97	...	-0.33	-0.86	-0.26	0.33	1.25	-0.06	0.38	-0.38	0.13	1.39
Max Drawdown	-0.21	-0.23	-0.21	-0.25	-0.23	-0.23	-0.23	-0.25	-0.23	-0.21	...	-0.15	-0.1	-0.22	-0.14	-0.21	-0.23	-0.21	-0.16	-0.22	-0.23
Longest DD Days	217	216	178	168	163	161	163	217	197	183	...	231	296	279	202	190	358	132	245	132	92
Volatility (ann.)	0.29	0.29	0.29	0.29	0.29	0.29	0.29	0.29	0.27	0.28	...	0.27	0.08	0.22	0.26	0.25	0.27	0.26	0.2	0.28	0.28
Calmar	1.28	0.61	0.88	0.43	-0.22	-0.13	-0.22	1.64	0.28	1.31	...	-0.76	-0.92	-0.35	0.35	1.48	-0.23	0.32	-0.62	-0.02	1.71
Skew	0.51	0.2	0.18	-0.18	0.34	0.33	0.34	0.5	-0.48	-0.54	...	0.33	-13.36	0.48	0.21	-0.16	0.02	0.14	-0.26	-0.03	0.09
Kurtosis	1.57	1.67	1.61	2.04	1.33	1.37	1.33	1.46	1.64	1.65	...	1.99	197.45	6.16	1.61	0.81	1.55	1.74	6.06	2.04	1.77
Expected Daily %	0.0	0.0	0.0	0.0	-0.0	-0.0	-0.0	0.0	0.0	0.0	...	-0.0	-0.0	-0.0	0.0	0.0	-0.0	0.0	-0.0	-0.0	0.0
Expected Monthly %	0.02	0.01	0.01	0.01	-0.0	-0.0	-0.0	0.03	0.01	0.02	...	-0.01	-0.01	-0.01	0.0	0.02	-0.0	0.01	-0.01	-0.0	0.03
Expected Yearly %	0.26	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.4	0.06	0.27	...	-0.11	-0.09	-0.08	0.05	0.31	-0.05	0.07	-0.1	-0.0	0.39
Kelly Criterion	0.08	0.05	0.06	0.04	-0.0	0.0	-0.0	0.1	0.04	0.09	...	-0.03	-1.11	-0.03	0.03	0.1	-0.01	0.03	-0.05	0.01	0.12
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	...	-0.03	-0.01	-0.02	-0.03	-0.03	-0.03	-0.03	-0.02	-0.03	-0.03
Expected Shortfall (cVaR)	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	...	-0.03	-0.01	-0.02	-0.03	-0.03	-0.03	-0.03	-0.02	-0.03	-0.03
Gain/Pain Ratio	0.18	0.11	0.13	0.09	-0.01	0.01	-0.01	0.26	0.07	0.19	...	-0.06	-0.75	-0.06	0.06	0.23	-0.01	0.07	-0.09	0.02	0.26
Gain/Pain (1M)	1.13	0.61	0.94	0.53	-0.03	0.03	-0.03	1.72	0.3	1.46	...	-0.34	-0.99	-0.22	0.41	1.71	-0.05	0.4	-0.3	0.12	1.91
Payoff Ratio	1.17	1.17	1.2	1.14	1.07	1.05	1.07	1.23	0.82	0.87	...	1.06	0.42	1.09	1.04	1.0	0.89	0.97	0.96	0.99	0.97
Profit Factor	1.18	1.11	1.13	1.09	0.99	1.01	0.99	1.26	1.07	1.19	...	0.94	0.25	0.94	1.06	1.23	0.99	1.07	0.91	1.02	1.26
Common Sense Ratio	1.36	1.08	1.17	1.18	1.17	1.17	1.17	1.51	0.97	1.22	...	0.99	NaN	0.92	1.11	1.25	0.85	1.09	0.83	1.04	1.33
CPC Index	0.7	0.63	0.66	0.61	0.51	0.52	0.51	0.78	0.49	0.6	...	0.47	0.04	0.48	0.56	0.68	0.46	0.54	0.42	0.51	0.69
Tail Ratio	1.15	0.98	1.03	1.09	1.17	1.16	1.17	1.2	0.91	1.03	...	1.05	NaN	0.97	1.04	1.02	0.86	1.02	0.91	1.02	1.06
Outlier Win Ratio	3.71	3.22	3.16	3.04	3.11	3.16	3.11	3.6	3.47	3.26	...	3.85	31.12	8.4	3.87	3.23	3.47	3.52	8.59	3.71	3.46
Outlier Loss Ratio	3.22	3.42	3.44	3.7	3.1	3.09	3.1	3.27	3.34	3.56	...	3.35	0.76	2.84	3.05	3.09	3.1	3.24	3.12	3.38	3.13
MTD	-0.01	0.01	0.03	0.02	-0.07	-0.07	-0.07	0.0	0.06	0.06	...	-0.04	0.0	-0.04	-0.03	0.06	0.06	0.06	0.02	0.01	0.06
3M	0.03	-0.08	0.04	-0.06	-0.09	-0.08	-0.09	0.04	-0.03	0.07	...	-0.05	0.0	-0.05	-0.1	0.07	-0.0	-0.05	0.01	-0.1	0.07
6M	-0.13	-0.16	-0.06	-0.07	-0.16	-0.14	-0.16	-0.16	0.12	0.24	...	-0.05	-0.01	-0.09	-0.05	0.24	-0.0	0.04	-0.03	-0.08	0.24
YTD	0.26	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.4	0.06	0.27	...	-0.11	-0.09	-0.08	0.05	0.31	-0.05	0.07	-0.1	-0.0	0.39
1Y	0.26	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.4	0.06	0.27	...	-0.11	-0.09	-0.08	0.05	0.31	-0.05	0.07	-0.1	-0.0	0.39
3Y (ann.)	0.27	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.41	0.06	0.27	...	-0.12	-0.09	-0.08	0.05	0.32	-0.05	0.07	-0.1	-0.0	0.4
5Y (ann.)	0.27	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.41	0.06	0.27	...	-0.12	-0.09	-0.08	0.05	0.32	-0.05	0.07	-0.1	-0.0	0.4
10Y (ann.)	0.27	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.41	0.06	0.27	...	-0.12	-0.09	-0.08	0.05	0.32	-0.05	0.07	-0.1	-0.0	0.4
All-time (ann.)	0.27	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.41	0.06	0.27	...	-0.12	-0.09	-0.08	0.05	0.32	-0.05	0.07	-0.1	-0.0	0.4
Best Day	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.05	0.05	...	0.07	0.01	0.07	0.07	0.05	0.07	0.07	0.06	0.07	0.07
Worst Day	-0.04	-0.06	-0.06	-0.08	-0.06	-0.06	-0.06	-0.04	-0.07	-0.07	...	-0.05	-0.07	-0.07	-0.05	-0.05	-0.05	-0.05	-0.07	-0.07	-0.06
Best Month	0.19	0.34	0.28	0.27	0.12	0.12	0.12	0.15	0.12	0.11	...	0.04	0.0	0.1	0.11	0.1	0.12	0.12	0.12	0.12	0.12
Worst Month	-0.11	-0.09	-0.09	-0.08	-0.1	-0.1	-0.1	-0.13	-0.13	-0.1	...	-0.06	-0.08	-0.06	-0.04	-0.08	-0.09	-0.08	-0.07	-0.13	-0.1
Best Year	0.26	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.4	0.06	0.27	...	-0.11	-0.09	-0.08	0.05	0.31	-0.05	0.07	-0.1	-0.0	0.39
Worst Year	0.26	0.14	0.18	0.11	-0.05	-0.03	-0.05	0.4	0.06	0.27	...	-0.11	-0.09	-0.08	0.05	0.31	-0.05	0.07	-0.1	-0.0	0.39
Avg. Drawdown	-0.05	-0.06	-0.06	-0.08	-0.06	-0.06	-0.06	-0.05	-0.04	-0.03	...	-0.09	-0.1	-0.07	-0.05	-0.04	-0.23	-0.04	-0.12	-0.06	-0.03
Avg. Drawdown Days	27	30	37	37	33	32	33	23	28	17	...	87	296	48	34	20	358	22	119	25	12
Recovery Factor	1.26	0.6	0.87	0.43	-0.22	-0.13	-0.22	1.61	0.28	1.29	...	-0.75	-0.91	-0.35	0.34	1.46	-0.23	0.32	-0.61	-0.02	1.68
Ulcer Index	1.11	1.13	1.13	1.13	1.13	1.13	1.13	1.1	1.42	1.13	...	1.08	inf	inf	1.05	1.11	1.09	1.06	1.05	1.45	1.11
Avg. Up Month	0.09	0.08	0.07	0.09	0.06	0.06	0.06	0.09	0.05	0.05	...	0.04	0.0	0.06	0.06	0.05	0.06	0.05	0.04	0.05	0.06

60 rows × 21 columns

S&P 500

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^GSPC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
41	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2009-01-05	2009-12-31	1.0	0.35	0.35	1.24	-0.22	0.07	-0.05
42	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2009-01-05	2009-12-31	1.0	0.21	0.21	0.83	-0.26	0.07	-0.05
43	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2009-01-05	2009-12-31	1.0	0.26	0.27	1.0	-0.26	0.07	-0.05
44	ts_conv1d_lstm_^GSPC	No	ts_conv1d_lstm	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
45	ts_wavenet_^GSPC	No	ts_wavenet	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
46	ts_wavenet_gru_^GSPC	No	ts_wavenet_gru	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
47	ts_wavenet_lstm_^GSPC	No	ts_wavenet_lstm	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
48	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2009-01-05	2009-12-31	1.0	0.31	0.32	1.14	-0.22	0.07	-0.05
49	ts_sma_1_^GSPC	No	ts_sma_1	^GSPC	2009-01-05	2009-12-31	0.98	0.06	0.07	0.38	-0.17	0.05	-0.06
50	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2009-01-05	2009-12-31	0.98	0.25	0.26	1.03	-0.19	0.05	-0.06
51	ts_bb_^GSPC	No	ts_bb	^GSPC	2009-01-05	2009-12-31	0.67	0.05	0.05	0.31	-0.18	0.06	-0.07
52	ts_macd_^GSPC	No	ts_macd	^GSPC	2009-01-05	2009-12-31	0.87	-0.21	-0.22	-0.85	-0.27	0.07	-0.05
53	ts_rsi_^GSPC	No	ts_rsi	^GSPC	2009-01-05	2009-12-31	0.08	0.1	0.1	1.08	-0.04	0.06	-0.04
54	ts_srsi_^GSPC	No	ts_srsi	^GSPC	2009-01-05	2009-12-31	0.47	-0.12	-0.12	-0.53	-0.2	0.07	-0.06
55	ts_adx_^GSPC	No	ts_adx	^GSPC	2009-01-05	2009-12-31	0.91	0.08	0.08	0.44	-0.17	0.07	-0.04
56	ts_kama_^GSPC	No	ts_kama	^GSPC	2009-01-05	2009-12-31	1.0	0.27	0.27	1.15	-0.22	0.04	-0.05
57	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2009-01-05	2009-12-31	0.97	0.08	0.08	0.42	-0.15	0.07	-0.04
58	ts_forceindex_^GSPC	No	ts_forceindex	^GSPC	2009-01-05	2009-12-31	0.93	0.03	0.03	0.23	-0.17	0.05	-0.05
59	ts_mfi_^GSPC	No	ts_mfi	^GSPC	2009-01-05	2009-12-31	0.44	-0.11	-0.11	-0.54	-0.18	0.05	-0.06
60	all_^GSPC	No	all	^GSPC	2009-01-05	2009-12-31	0.98	0.32	0.33	1.18	-0.2	0.07	-0.05
61	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^GSPC" ').T.head(60)

	41	42	43	44	45	46	47	48	49	50	...	52	53	54	55	56	57	58	59	60	61
ta_reg_item_name	ts_gru_1_^GSPC	ts_lstm_^GSPC	ts_conv1d_gru_^GSPC	ts_conv1d_lstm_^GSPC	ts_wavenet_^GSPC	ts_wavenet_gru_^GSPC	ts_wavenet_lstm_^GSPC	ts_gru_2_^GSPC	ts_sma_1_^GSPC	ts_sma_2_^GSPC	...	ts_macd_^GSPC	ts_rsi_^GSPC	ts_srsi_^GSPC	ts_adx_^GSPC	ts_kama_^GSPC	ts_tsi_^GSPC	ts_forceindex_^GSPC	ts_mfi_^GSPC	all_^GSPC	Long-Only Trade Strategy for ^GSPC
ta_reg_item_id	None	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
is_portfolio	No	No	No	No	No	No	No	No	No	No	...	No	No	No	No	No	No	No	No	No	No
model_type	ts_gru_1	ts_lstm	ts_conv1d_gru	ts_conv1d_lstm	ts_wavenet	ts_wavenet_gru	ts_wavenet_lstm	ts_gru_2	ts_sma_1	ts_sma_2	...	ts_macd	ts_rsi	ts_srsi	ts_adx	ts_kama	ts_tsi	ts_forceindex	ts_mfi	all	long
trade_asset	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	...	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC
test_period_len_days	364	364	364	364	364	364	364	364	364	364	...	364	364	364	364	364	364	364	364	364	364
tr_period_len_days	2190	2190	2190	2190	2190	2190	2190	2190	2190	2190	...	2190	2190	2190	2190	2190	2190	2190	2190	2190	2190
val_period_len_days	730	730	730	730	730	730	730	730	730	730	...	730	730	730	730	730	730	730	730	730	730
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	...	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	...	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05	2009-01-05
End Period	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	...	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31	2009-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.98	0.98	...	0.87	0.08	0.47	0.91	1.0	0.97	0.93	0.44	0.98	1.0
Cumulative Return	0.35	0.21	0.26	0.2	0.2	0.2	0.2	0.31	0.06	0.25	...	-0.21	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.32	0.2
CAGR%	0.35	0.21	0.27	0.2	0.2	0.2	0.2	0.32	0.07	0.26	...	-0.22	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.33	0.2
Sharpe	1.24	0.83	1.0	0.8	0.8	0.8	0.8	1.14	0.38	1.03	...	-0.85	1.08	-0.53	0.44	1.15	0.42	0.23	-0.54	1.18	0.8
Sortino	1.86	1.21	1.47	1.16	1.16	1.16	1.16	1.69	0.52	1.47	...	-1.21	2.28	-0.76	0.67	1.7	0.62	0.32	-0.72	1.76	1.16
Sortino/√2	1.31	0.85	1.04	0.82	0.82	0.82	0.82	1.19	0.37	1.04	...	-0.85	1.61	-0.54	0.48	1.2	0.44	0.23	-0.51	1.25	0.82
Max Drawdown	-0.22	-0.26	-0.26	-0.28	-0.28	-0.28	-0.28	-0.22	-0.17	-0.19	...	-0.27	-0.04	-0.2	-0.17	-0.22	-0.15	-0.17	-0.18	-0.2	-0.28
Longest DD Days	59	117	117	145	145	145	145	100	122	218	...	255	140	279	296	289	199	131	344	92	145
Volatility (ann.)	0.27	0.27	0.27	0.27	0.27	0.27	0.27	0.27	0.25	0.25	...	0.25	0.09	0.2	0.24	0.23	0.25	0.24	0.19	0.27	0.27
Calmar	1.58	0.81	1.04	0.72	0.72	0.72	0.72	1.44	0.38	1.38	...	-0.81	2.58	-0.59	0.49	1.25	0.53	0.16	-0.65	1.64	0.72
Skew	0.07	0.07	0.04	0.04	0.04	0.04	0.04	0.06	-0.36	-0.29	...	0.42	5.3	0.6	0.47	-0.06	0.3	-0.12	-0.34	0.09	0.04
Kurtosis	2.07	2.07	2.08	2.07	2.07	2.07	2.07	2.08	1.6	1.57	...	2.49	71.25	8.65	2.0	0.76	1.99	1.21	6.44	2.34	2.07
Expected Daily %	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	-0.0	0.0	-0.0	0.0	0.0	0.0	0.0	-0.0	0.0	0.0
Expected Monthly %	0.03	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.01	0.02	...	-0.02	0.01	-0.01	0.01	0.02	0.01	0.0	-0.01	0.02	0.02
Expected Yearly %	0.35	0.21	0.26	0.2	0.2	0.2	0.2	0.31	0.06	0.25	...	-0.21	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.32	0.2
Kelly Criterion	0.11	0.07	0.09	0.07	0.07	0.07	0.07	0.1	0.04	0.09	...	-0.08	0.33	-0.06	0.04	0.09	0.04	0.02	-0.07	0.1	0.07
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.02	...	-0.03	-0.01	-0.02	-0.02	-0.02	-0.03	-0.02	-0.02	-0.03	-0.03
Expected Shortfall (cVaR)	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.02	...	-0.03	-0.01	-0.02	-0.02	-0.02	-0.03	-0.02	-0.02	-0.03	-0.03
Gain/Pain Ratio	0.24	0.16	0.19	0.15	0.15	0.15	0.15	0.22	0.07	0.2	...	-0.15	1.3	-0.13	0.08	0.21	0.08	0.04	-0.13	0.24	0.15
Gain/Pain (1M)	2.49	1.17	1.42	0.88	0.88	0.88	0.88	1.83	0.33	2.75	...	-0.62	3.44	-0.54	0.6	1.68	0.5	0.25	-0.38	1.84	0.88
Payoff Ratio	1.0	0.95	0.95	0.93	0.93	0.93	0.93	1.03	0.86	1.0	...	1.08	1.67	1.23	1.17	1.05	1.0	0.98	1.08	1.01	0.93
Profit Factor	1.24	1.16	1.19	1.15	1.15	1.15	1.15	1.22	1.07	1.2	...	0.85	2.3	0.87	1.08	1.21	1.08	1.04	0.87	1.24	1.15
Common Sense Ratio	1.38	1.17	1.21	1.07	1.07	1.07	1.07	1.24	0.91	1.16	...	0.85	NaN	0.77	1.22	1.27	1.05	1.02	0.73	1.25	1.07
CPC Index	0.69	0.6	0.63	0.59	0.59	0.59	0.59	0.68	0.51	0.65	...	0.41	2.23	0.45	0.61	0.68	0.56	0.53	0.42	0.69	0.59
Tail Ratio	1.11	1.01	1.01	0.93	0.93	0.93	0.93	1.01	0.86	0.97	...	0.99	NaN	0.89	1.13	1.04	0.98	0.98	0.83	1.01	0.93
Outlier Win Ratio	3.42	3.51	3.52	3.55	3.55	3.55	3.55	3.38	3.59	3.33	...	4.4	24.7	9.97	3.94	3.34	3.8	3.79	9.97	3.7	3.55
Outlier Loss Ratio	3.76	3.64	3.64	3.6	3.6	3.6	3.6	3.82	3.39	3.64	...	3.36	0.67	3.22	3.16	3.0	3.17	3.41	3.12	3.82	3.6
MTD	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.01	0.02	0.02	...	-0.04	0.0	-0.01	-0.03	0.02	0.02	0.0	-0.02	0.02	0.02
3M	0.05	0.05	0.05	0.05	0.05	0.05	0.05	0.04	0.03	0.05	...	-0.11	0.0	-0.04	-0.06	0.05	-0.02	-0.08	-0.04	0.05	0.05
6M	0.19	0.16	0.21	0.21	0.21	0.21	0.21	0.17	0.02	0.12	...	-0.16	0.01	-0.04	0.0	0.21	0.03	0.03	-0.02	0.21	0.21
YTD	0.35	0.21	0.26	0.2	0.2	0.2	0.2	0.31	0.06	0.25	...	-0.21	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.32	0.2
1Y	0.35	0.21	0.26	0.2	0.2	0.2	0.2	0.31	0.06	0.25	...	-0.21	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.32	0.2
3Y (ann.)	0.35	0.21	0.27	0.2	0.2	0.2	0.2	0.32	0.07	0.26	...	-0.22	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.33	0.2
5Y (ann.)	0.35	0.21	0.27	0.2	0.2	0.2	0.2	0.32	0.07	0.26	...	-0.22	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.33	0.2
10Y (ann.)	0.35	0.21	0.27	0.2	0.2	0.2	0.2	0.32	0.07	0.26	...	-0.22	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.33	0.2
All-time (ann.)	0.35	0.21	0.27	0.2	0.2	0.2	0.2	0.32	0.07	0.26	...	-0.22	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.33	0.2
Best Day	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.07	0.05	0.05	...	0.07	0.06	0.07	0.07	0.04	0.07	0.05	0.05	0.07	0.07
Worst Day	-0.05	-0.05	-0.05	-0.05	-0.05	-0.05	-0.05	-0.05	-0.06	-0.06	...	-0.05	-0.04	-0.06	-0.04	-0.05	-0.04	-0.05	-0.06	-0.05	-0.05
Best Month	0.11	0.09	0.09	0.09	0.09	0.09	0.09	0.11	0.11	0.11	...	0.04	0.13	0.05	0.09	0.11	0.09	0.09	0.05	0.11	0.09
Worst Month	-0.11	-0.11	-0.11	-0.11	-0.11	-0.11	-0.11	-0.09	-0.12	-0.07	...	-0.07	-0.03	-0.09	-0.05	-0.07	-0.06	-0.09	-0.09	-0.08	-0.11
Best Year	0.35	0.21	0.26	0.2	0.2	0.2	0.2	0.31	0.06	0.25	...	-0.21	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.32	0.2
Worst Year	0.35	0.21	0.26	0.2	0.2	0.2	0.2	0.31	0.06	0.25	...	-0.21	0.1	-0.12	0.08	0.27	0.08	0.03	-0.11	0.32	0.2
Avg. Drawdown	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.03	-0.06	-0.03	...	-0.09	-0.02	-0.06	-0.04	-0.04	-0.05	-0.04	-0.09	-0.03	-0.03
Avg. Drawdown Days	11	14	13	15	15	15	15	13	31	23	...	43	33	50	43	33	34	28	174	11	15
Recovery Factor	1.56	0.8	1.03	0.71	0.71	0.71	0.71	1.42	0.38	1.36	...	-0.8	2.54	-0.58	0.48	1.23	0.52	0.16	-0.64	1.62	0.71
Ulcer Index	1.05	1.06	1.06	1.26	1.26	1.26	1.26	1.42	1.44	1.05	...	1.13	inf	inf	1.06	1.04	1.11	1.1	1.02	1.41	1.26
Avg. Up Month	0.06	0.04	0.05	0.05	0.05	0.05	0.05	0.06	0.05	0.04	...	0.03	0.03	0.02	0.05	0.05	0.04	0.04	0.03	0.05	0.05

60 rows × 21 columns

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Portfolio H

Portfolio H: Utilized Leveraged ETFs for Trading on The Model Security

- All models are trained on their corresponding US Index/model, but the trade enter via a broker is with
  the underlying leveraged ETF.

- ETF data for US Indixes not available until 2011 in yahoo finance

- Five Trade Strategies are executed with ETF on, and Five with ETF off to show effect of ETF trading

- Additionally, there is a composite trade strategy for all ETF strategies and one for non-ETF 
  trade strategies

Backtest:

1. Type: regular

2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2007-01-01 
        2. End Date   = 2012-12-31  
    2. Validation Period: 
        1. Begin Date = 2013-01-01 
        2. End Date   = 2014-12-31 

3. Test Period: 
    1. Begin Date = 2015-01-01 
    2. End Date   = 2015-12-31         

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 20:53:17,844 [INFO] root: Found previous Market data previously loaded to specified file and dir.

MarketData.getDateRangesForStocks( ts_marketdat_mng.marketDataObj.getDataFrame() )

	Index	Beg_Date	End_Date
0	^DJI	2000-01-03	2021-09-20
1	^GSPC	2000-01-03	2021-09-20
2	^IXIC	2000-01-03	2021-09-20
3	SPXL	2008-11-05	2021-09-20
4	SPXS	2008-11-19	2021-09-20
5	SDOW	2010-02-11	2021-09-20
6	SQQQ	2010-02-11	2021-09-20
7	TQQQ	2010-02-11	2021-09-20
8	UDOW	2010-02-11	2021-09-20

Pipeline Data Manager

ts_model_pipline_mng_h = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_H",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )

# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_bb",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "bb",
                                  'type'     : "ta",
                                  'obj_args' : { 'window' : 13, 'window_dev' : 1 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_macd",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "macd",
                                  'type'     : "ta",
                                  'obj_args' : { 'window_slow' : 26, 'window_fast' : 12, 'window_sign' : 9,
                                                 'enter_long_macd_diff'  : 0.5 , 'exi_long_macd_diff' : 0.5,
                                                 'enter_short_macd_diff' : -0.5, 'exi_short_macd_diff' : -0.5  },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_rsi",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "rsi",
                                  'type'     : "ta",
                                  'obj_args' : { 'rsi_enter_long'  : 30.0,  'rsi_exit_long'  : 30.0, 
                                                 'rsi_enter_short' : 70.0,  'rsi_exit_short' : 70.0 },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

# **** Configure ETF based Trade Strategies

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_h )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_h )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        trade_on_ETFs          =  True,
                        trade_assets_etf_map   = { "^DJI"  : [ "UDOW", "SDOW"], 
                                                   "^IXIC" : [ "TQQQ", "SQQQ"], 
                                                   "^GSPC" : [ "SPXL", "SPXS"] }, 
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}_etf".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

# **** Configure Non-ETF based Trade Strategies (for comparison)
        
for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_h )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_h )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        trade_on_ETFs          =  False,
                        trade_assets_etf_map   = { "^DJI"  : [ "UDOW", "SDOW"], 
                                                   "^IXIC" : [ "TQQQ", "SQQQ"], 
                                                   "^GSPC" : [ "SPXL", "SPXS"] }, 
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)
        

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  36

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Using ETFs for Trading",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio H, US-Indexes - Using ETFs for Trading, and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 20:56:22,949 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio H, US-Indexes - Using ETFs for Trading, and Long strategies" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2006-07-01",
                             strBegDate         = "2007-01-01",
                             strEndDate         = "2012-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2012-07-01",
                               strBegDate         = "2013-01-01",
                               strEndDate         = "2014-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2014-07-01",
                               strBegDate         = "2015-01-01",
                               strEndDate         = "2015-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio H",
                    timePeriod  = testing_period,
                    fitDatasets = fit_datasets,
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Trade Strategies Execution & Investment Metrics

DOW Jones

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("DJI")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
1	ts_gru_^DJI_etf	No	85	2000000.0	1.971196e+06	-0.014402	Completed
2	ts_lstm_^DJI_etf	No	31	2000000.0	1.889744e+06	-0.055128	Completed
3	ts_bb_^DJI_etf	No	60	2000000.0	2.855684e+06	0.427842	Completed
4	ts_macd_^DJI_etf	No	21	2000000.0	2.433666e+06	0.216833	Completed
5	ts_rsi_^DJI_etf	No	4	2000000.0	2.025475e+06	0.012738	Completed
6	all_^DJI_etf	No	103	2000000.0	1.801438e+06	-0.099281	Completed
19	ts_gru_^DJI	No	85	2000000.0	2.269268e+06	0.134634	Completed
20	ts_lstm_^DJI	No	31	2000000.0	2.466724e+06	0.233362	Completed
21	ts_bb_^DJI	No	60	2000000.0	2.365050e+06	0.182525	Completed
22	ts_macd_^DJI	No	21	2000000.0	1.868237e+06	-0.065882	Completed
23	ts_rsi_^DJI	No	4	2000000.0	2.087736e+06	0.043868	Completed
24	all_^DJI	No	103	2000000.0	2.484117e+06	0.242058	Completed
39	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	1.954308e+06	-0.022846	Completed

Nasdaq

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("IXIC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
7	ts_gru_^IXIC_etf	No	59	2000000.0	2.486847e+06	0.243423	Completed
8	ts_lstm_^IXIC_etf	No	23	2000000.0	2.596818e+06	0.298409	Completed
9	ts_bb_^IXIC_etf	No	54	2000000.0	2.479309e+06	0.239655	Completed
10	ts_macd_^IXIC_etf	No	22	2000000.0	3.120366e+06	0.560183	Completed
11	ts_rsi_^IXIC_etf	No	4	2000000.0	2.133742e+06	0.066871	Completed
12	all_^IXIC_etf	No	71	2000000.0	2.261971e+06	0.130986	Completed
25	ts_gru_^IXIC	No	59	2000000.0	1.849588e+06	-0.075206	Completed
26	ts_lstm_^IXIC	No	23	2000000.0	2.381274e+06	0.190637	Completed
27	ts_bb_^IXIC	No	54	2000000.0	2.589623e+06	0.294811	Completed
28	ts_macd_^IXIC	No	22	2000000.0	1.694798e+06	-0.152601	Completed
29	ts_rsi_^IXIC	No	4	2000000.0	2.100902e+06	0.050451	Completed
30	all_^IXIC	No	71	2000000.0	1.925470e+06	-0.037265	Completed
38	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	2.118694e+06	0.059347	Completed

S&P 500

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("GSPC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
13	ts_gru_^GSPC_etf	No	75	2000000.0	1.980361e+06	-0.009820	Completed
14	ts_lstm_^GSPC_etf	No	24	2000000.0	2.060605e+06	0.030303	Completed
15	ts_bb_^GSPC_etf	No	59	2000000.0	2.556850e+06	0.278425	Completed
16	ts_macd_^GSPC_etf	No	31	2000000.0	2.483369e+06	0.241685	Completed
17	ts_rsi_^GSPC_etf	No	3	2000000.0	2.016714e+06	0.008357	Completed
18	all_^GSPC_etf	No	93	2000000.0	1.860754e+06	-0.069623	Completed
31	ts_gru_^GSPC	No	75	2000000.0	2.303542e+06	0.151771	Completed
32	ts_lstm_^GSPC	No	24	2000000.0	2.430980e+06	0.215490	Completed
33	ts_bb_^GSPC	No	59	2000000.0	2.233747e+06	0.116873	Completed
34	ts_macd_^GSPC	No	31	2000000.0	1.612727e+06	-0.193636	Completed
35	ts_rsi_^GSPC	No	3	2000000.0	2.078932e+06	0.039466	Completed
36	all_^GSPC	No	93	2000000.0	2.134662e+06	0.067331	Completed
37	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	1.986154e+06	-0.006923	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^DJI"' )

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
1	ts_gru_^DJI_etf	No	ts_gru	^DJI	2015-01-05	2015-12-31	0.99	-0.01	-0.01	0.19	-0.36	0.12	-0.11
2	ts_lstm_^DJI_etf	No	ts_lstm	^DJI	2015-01-05	2015-12-31	0.99	-0.06	-0.06	0.1	-0.37	0.12	-0.11
3	ts_bb_^DJI_etf	No	ts_bb	^DJI	2015-01-05	2015-12-31	0.65	0.43	0.43	1.13	-0.29	0.12	-0.11
4	ts_macd_^DJI_etf	No	ts_macd	^DJI	2015-01-05	2015-12-31	0.91	0.22	0.22	0.64	-0.33	0.2	-0.13
5	ts_rsi_^DJI_etf	No	ts_rsi	^DJI	2015-01-05	2015-12-31	0.02	0.01	0.01	0.16	-0.14	0.12	-0.11
6	all_^DJI_etf	No	all	^DJI	2015-01-05	2015-12-31	0.91	-0.1	-0.1	-0.03	-0.39	0.12	-0.11
19	ts_gru_^DJI	No	ts_gru	^DJI	2015-01-05	2015-12-31	1.0	0.13	0.14	0.9	-0.11	0.04	-0.04
20	ts_lstm_^DJI	No	ts_lstm	^DJI	2015-01-05	2015-12-31	1.0	0.23	0.24	1.45	-0.12	0.04	-0.04
21	ts_bb_^DJI	No	ts_bb	^DJI	2015-01-05	2015-12-31	0.65	0.18	0.19	1.36	-0.08	0.04	-0.04
22	ts_macd_^DJI	No	ts_macd	^DJI	2015-01-05	2015-12-31	0.92	-0.07	-0.07	-0.4	-0.15	0.03	-0.03
23	ts_rsi_^DJI	No	ts_rsi	^DJI	2015-01-05	2015-12-31	0.02	0.04	0.04	0.75	-0.02	0.04	-0.02
24	all_^DJI	No	all	^DJI	2015-01-05	2015-12-31	0.92	0.24	0.25	1.57	-0.1	0.04	-0.04
39	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2015-01-05	2015-12-31	1.0	-0.02	-0.02	-0.07	-0.14	0.04	-0.04

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^IXIC"' )

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
7	ts_gru_^IXIC_etf	No	ts_gru	^IXIC	2015-01-05	2015-12-31	1.0	0.24	0.25	0.68	-0.36	0.14	-0.12
8	ts_lstm_^IXIC_etf	No	ts_lstm	^IXIC	2015-01-05	2015-12-31	1.0	0.3	0.3	0.76	-0.36	0.14	-0.12
9	ts_bb_^IXIC_etf	No	ts_bb	^IXIC	2015-01-05	2015-12-31	0.63	0.24	0.24	0.71	-0.35	0.14	-0.12
10	ts_macd_^IXIC_etf	No	ts_macd	^IXIC	2015-01-05	2015-12-31	0.93	0.56	0.57	1.0	-0.44	0.34	-0.19
11	ts_rsi_^IXIC_etf	No	ts_rsi	^IXIC	2015-01-05	2015-12-31	0.04	0.07	0.07	0.42	-0.14	0.14	-0.12
12	all_^IXIC_etf	No	all	^IXIC	2015-01-05	2015-12-31	0.93	0.13	0.13	0.5	-0.39	0.14	-0.12
25	ts_gru_^IXIC	No	ts_gru	^IXIC	2015-01-05	2015-12-31	1.0	-0.08	-0.08	-0.38	-0.19	0.04	-0.04
26	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2015-01-05	2015-12-31	1.0	0.19	0.19	1.12	-0.1	0.04	-0.04
27	ts_bb_^IXIC	No	ts_bb	^IXIC	2015-01-05	2015-12-31	0.63	0.29	0.3	1.89	-0.08	0.04	-0.04
28	ts_macd_^IXIC	No	ts_macd	^IXIC	2015-01-05	2015-12-31	0.93	-0.15	-0.15	-0.99	-0.21	0.03	-0.03
29	ts_rsi_^IXIC	No	ts_rsi	^IXIC	2015-01-05	2015-12-31	0.04	0.05	0.05	0.82	-0.02	0.04	-0.02
30	all_^IXIC	No	all	^IXIC	2015-01-05	2015-12-31	0.93	-0.04	-0.04	-0.15	-0.16	0.04	-0.04
38	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2015-01-05	2015-12-31	1.0	0.06	0.06	0.43	-0.14	0.04	-0.04

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^GSPC"' )

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
13	ts_gru_^GSPC_etf	No	ts_gru	^GSPC	2015-01-05	2015-12-31	1.0	-0.01	-0.01	0.21	-0.33	0.12	-0.12
14	ts_lstm_^GSPC_etf	No	ts_lstm	^GSPC	2015-01-05	2015-12-31	1.0	0.03	0.03	0.29	-0.32	0.12	-0.12
15	ts_bb_^GSPC_etf	No	ts_bb	^GSPC	2015-01-05	2015-12-31	0.64	0.28	0.28	0.83	-0.3	0.12	-0.12
16	ts_macd_^GSPC_etf	No	ts_macd	^GSPC	2015-01-05	2015-12-31	0.87	0.24	0.25	0.68	-0.29	0.21	-0.15
17	ts_rsi_^GSPC_etf	No	ts_rsi	^GSPC	2015-01-05	2015-12-31	0.02	0.01	0.01	0.14	-0.16	0.12	-0.13
18	all_^GSPC_etf	No	all	^GSPC	2015-01-05	2015-12-31	0.93	-0.07	-0.07	0.05	-0.39	0.12	-0.12
31	ts_gru_^GSPC	No	ts_gru	^GSPC	2015-01-05	2015-12-31	1.0	0.15	0.15	0.99	-0.1	0.04	-0.04
32	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2015-01-05	2015-12-31	1.0	0.22	0.22	1.33	-0.1	0.04	-0.04
33	ts_bb_^GSPC	No	ts_bb	^GSPC	2015-01-05	2015-12-31	0.64	0.12	0.12	0.9	-0.08	0.04	-0.04
34	ts_macd_^GSPC	No	ts_macd	^GSPC	2015-01-05	2015-12-31	0.87	-0.19	-0.2	-1.44	-0.22	0.04	-0.03
35	ts_rsi_^GSPC	No	ts_rsi	^GSPC	2015-01-05	2015-12-31	0.02	0.04	0.04	0.66	-0.02	0.04	-0.02
36	all_^GSPC	No	all	^GSPC	2015-01-05	2015-12-31	0.93	0.07	0.07	0.51	-0.08	0.04	-0.04
37	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2015-01-05	2015-12-31	1.0	-0.01	-0.01	0.03	-0.12	0.04	-0.04

tsExecuteEngine.getSummaryMetrics().T.head(60)

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Portfolio I

Portfolio I: Monthly Rebalance of Funds in the Portfolio

- Same strategies as defined and explained in Portfolio C.

- Only difference is the Portfolio Investment Manager is configured to balance funds monthly, 
  using an equal weights policy.

Backtest:

1. Type: regular

2. Test Period: 
    1. Begin Date = 2012-01-01 
    2. End Date   = 2015-12-31   

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 21:40:46,355 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_1",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 20, 'win_slow' : 100 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_3",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_4",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_5",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 75 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #6

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_6",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 7, 'win_slow' : 35 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #7

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_7",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 7, 'win_slow' : 35 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = None )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = None )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  24

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Montly-Rebalance TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Monthly, # Monthly Rebalance
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio Inv. Manager for US-Indexes - Montly-Rebalance TS",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio Inv. Manager for US-Indexes - Montly-Rebalance TS" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio I",
                    timePeriod = DatasetDef(datasetType = DatasetType.Testing,
                                            strBegDate = "2012-01-01", 
                                            strEndDate = "2015-12-31" ),
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']]

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
0	Portfolio Inv. Manager for US-Indexes - Montly...	Yes	684	54000000.0	5.421659e+07	0.004011	Completed
1	ts_sma_1_^DJI	No	13	2250000.0	2.255893e+06	0.002619	Completed
2	ts_sma_2_^DJI	No	26	2250000.0	2.255893e+06	0.002619	Completed
3	ts_sma_3_^DJI	No	26	2250000.0	2.255893e+06	0.002619	Completed
4	ts_sma_4_^DJI	No	24	2250000.0	2.293478e+06	0.019324	Completed
5	ts_sma_5_^DJI	No	19	2250000.0	2.255893e+06	0.002619	Completed
6	ts_sma_6_^DJI	No	34	2250000.0	2.255893e+06	0.002619	Completed
7	ts_sma_7_^DJI	No	34	2250000.0	2.255893e+06	0.002619	Completed
8	all_^DJI	No	53	2250000.0	2.293478e+06	0.019324	Completed
9	ts_sma_1_^IXIC	No	11	2250000.0	2.255893e+06	0.002619	Completed
10	ts_sma_2_^IXIC	No	23	2250000.0	2.255893e+06	0.002619	Completed
11	ts_sma_3_^IXIC	No	23	2250000.0	2.255893e+06	0.002619	Completed
12	ts_sma_4_^IXIC	No	16	2250000.0	2.255893e+06	0.002619	Completed
13	ts_sma_5_^IXIC	No	13	2250000.0	2.255893e+06	0.002619	Completed
14	ts_sma_6_^IXIC	No	36	2250000.0	2.255893e+06	0.002619	Completed
15	ts_sma_7_^IXIC	No	36	2250000.0	2.255893e+06	0.002619	Completed
16	all_^IXIC	No	57	2250000.0	2.255893e+06	0.002619	Completed
17	ts_sma_1_^GSPC	No	11	2250000.0	2.255893e+06	0.002619	Completed
18	ts_sma_2_^GSPC	No	32	2250000.0	2.255893e+06	0.002619	Completed
19	ts_sma_3_^GSPC	No	32	2250000.0	2.255893e+06	0.002619	Completed
20	ts_sma_4_^GSPC	No	22	2250000.0	2.255893e+06	0.002619	Completed
21	ts_sma_5_^GSPC	No	17	2250000.0	2.255893e+06	0.002619	Completed
22	ts_sma_6_^GSPC	No	36	2250000.0	2.255893e+06	0.002619	Completed
23	ts_sma_7_^GSPC	No	36	2250000.0	2.255893e+06	0.002619	Completed
24	all_^GSPC	No	54	2250000.0	2.255893e+06	0.002619	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio Inv. Manager for US-Indexes - Montly...	Yes			2012-01-04	2015-12-31	0.71	0.0	0.0	0.04	-0.15	0.02	-0.02
1	ts_sma_1_^DJI	No	ts_sma_1	^DJI	2012-01-04	2015-12-31	0.22	0.0	0.0	0.04	-0.15	0.06	-0.03
2	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2012-01-04	2015-12-31	0.28	0.0	0.0	0.05	-0.15	0.04	-0.03
3	ts_sma_3_^DJI	No	ts_sma_3	^DJI	2012-01-04	2015-12-31	0.27	0.0	0.0	0.04	-0.15	0.04	-0.03
4	ts_sma_4_^DJI	No	ts_sma_4	^DJI	2012-01-04	2015-12-31	0.31	0.02	0.0	0.1	-0.16	0.04	-0.03
5	ts_sma_5_^DJI	No	ts_sma_5	^DJI	2012-01-04	2015-12-31	0.27	0.0	0.0	0.05	-0.15	0.04	-0.03
6	ts_sma_6_^DJI	No	ts_sma_6	^DJI	2012-01-04	2015-12-31	0.3	0.0	0.0	0.05	-0.17	0.05	-0.05
7	ts_sma_7_^DJI	No	ts_sma_7	^DJI	2012-01-04	2015-12-31	0.3	0.0	0.0	0.05	-0.16	0.04	-0.05
8	all_^DJI	No	all	^DJI	2012-01-04	2015-12-31	0.51	0.02	0.0	0.1	-0.15	0.06	-0.03
9	ts_sma_1_^IXIC	No	ts_sma_1	^IXIC	2012-01-04	2015-12-31	0.21	0.0	0.0	0.05	-0.19	0.06	-0.04
10	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2012-01-04	2015-12-31	0.25	0.0	0.0	0.05	-0.2	0.04	-0.04
11	ts_sma_3_^IXIC	No	ts_sma_3	^IXIC	2012-01-04	2015-12-31	0.24	0.0	0.0	0.05	-0.21	0.05	-0.04
12	ts_sma_4_^IXIC	No	ts_sma_4	^IXIC	2012-01-04	2015-12-31	0.2	0.0	0.0	0.05	-0.17	0.05	-0.03
13	ts_sma_5_^IXIC	No	ts_sma_5	^IXIC	2012-01-04	2015-12-31	0.19	0.0	0.0	0.05	-0.15	0.06	-0.03
14	ts_sma_6_^IXIC	No	ts_sma_6	^IXIC	2012-01-04	2015-12-31	0.32	0.0	0.0	0.06	-0.19	0.04	-0.05
15	ts_sma_7_^IXIC	No	ts_sma_7	^IXIC	2012-01-04	2015-12-31	0.3	0.0	0.0	0.05	-0.19	0.04	-0.05
16	all_^IXIC	No	all	^IXIC	2012-01-04	2015-12-31	0.47	0.0	0.0	0.07	-0.22	0.05	-0.04
17	ts_sma_1_^GSPC	No	ts_sma_1	^GSPC	2012-01-04	2015-12-31	0.2	0.0	0.0	0.04	-0.17	0.06	-0.03
18	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2012-01-04	2015-12-31	0.29	0.0	0.0	0.05	-0.17	0.05	-0.05
19	ts_sma_3_^GSPC	No	ts_sma_3	^GSPC	2012-01-04	2015-12-31	0.28	0.0	0.0	0.05	-0.16	0.04	-0.05
20	ts_sma_4_^GSPC	No	ts_sma_4	^GSPC	2012-01-04	2015-12-31	0.27	0.0	0.0	0.05	-0.16	0.04	-0.03
21	ts_sma_5_^GSPC	No	ts_sma_5	^GSPC	2012-01-04	2015-12-31	0.22	0.0	0.0	0.04	-0.18	0.03	-0.03
22	ts_sma_6_^GSPC	No	ts_sma_6	^GSPC	2012-01-04	2015-12-31	0.3	0.0	0.0	0.05	-0.2	0.04	-0.06
23	ts_sma_7_^GSPC	No	ts_sma_7	^GSPC	2012-01-04	2015-12-31	0.28	0.0	0.0	0.05	-0.2	0.05	-0.06
24	all_^GSPC	No	all	^GSPC	2012-01-04	2015-12-31	0.45	0.0	0.0	0.05	-0.17	0.05	-0.03

tsExecuteEngine.getSummaryMetrics().T.head(60)

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Portfolio J

Portfolio J: Trade Strategies with different Market Data Sources (Quandal, YahooFinance, User-Defined DataFrame)

- Futures data is sourced from Quandal (Cotton, Soy Beans, and S&P 500 futures)

- US Equity indexes from Yahoo-Finance

- Leverage ETFs TQQQ and SQQQ from the user-defined DataFrame

- Multiple SMA Models, and LSTM, GRU, MACD, and RSI Models defined to use with each trading asset

- Additionally, there is a composite trade strategy for all Futures, and one composite trade
  strategy for the US Index equities

- Buy-and-Hold strategies for each trade asset for comparison

Backtest:

1. Type: regular

2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2006-12-31  
    2. Validation Period: 
        1. Begin Date = 2007-01-01 
        2. End Date   = 2008-06-30 

3. Test Period: 
    1. Begin Date = 2009-01-01 
    2. End Date   = 2009-12-31         

Trade Assets - Yahoo-Finance

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager - Yahoo-Finance

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "2000-01-01"
end_date_str           = None

ts_marketdat_mng_yf = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 22:14:56,355 [INFO] root: Found previous Market data previously loaded to specified file and dir.

ts_marketdat_mng_yf.marketDataObj.getDataFrame()

	Index	Adj Close	Close	High	Low	Open	Volume
Date
2000-01-03	^DJI	11357.509766	11357.509766	11522.009766	11305.690430	11501.849609	169750000
2000-01-03	^GSPC	1455.219971	1455.219971	1478.000000	1438.359985	1469.250000	931800000
2000-01-03	^IXIC	4131.149902	4131.149902	4192.189941	3989.709961	4186.189941	1510070000
2000-01-04	^DJI	10997.929688	10997.929688	11350.059570	10986.450195	11349.750000	178420000
2000-01-04	^GSPC	1399.420044	1399.420044	1455.219971	1397.430054	1455.219971	1009000000
...	...	...	...	...	...	...	...
2021-09-20	TQQQ	133.490005	133.490005	137.460007	128.000000	135.419998	60721113
2021-09-20	UDOW	69.760002	69.760002	71.459999	67.519997	70.220001	6439708
2021-09-20	^DJI	33970.468750	33970.468750	34459.718750	33613.031250	34459.718750	441645657
2021-09-20	^GSPC	4357.729980	4357.729980	4402.950195	4305.910156	4402.950195	2598257000
2021-09-20	^IXIC	14713.903320	14713.903320	14841.815430	14530.073242	14758.099609	4350941000

34549 rows × 7 columns

Trade Assets - From User-Defined Pandas DataFrame

User can provide their own market data via a Pandas DataFrame

Requirements:

1. DataFrame Index : must be called "Date", and be of datetime format
2. Index column : All data associated with a given trade asset. Same exact format as the 
   yahoofinance above
3. The rest of the columns in the dataframe can be named anything. However, it is recommended
   they used standard names as the ones in YahooFinance above; this will make all built-in 
   AI and Technical Analysis Models available.
4. Note, interval, supportedTradeAssets are constraint and not enforced; the data that is passed
   in the dataframe is what is supported.

ts_marketdat_mng_df = TradeStrategyMarketDataMng(\
        marketDataProviderName = "dataframe",
        marketDataDF           =  ts_marketdat_mng_yf.marketDataObj.getDataFrame(),                     
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_DF",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-28 22:15:09,740 [INFO] root: Found previous Market data previously loaded to specified file and dir.

ts_marketdat_mng_df.marketDataObj.getDataFrame()

	Index	Adj Close	Close	High	Low	Open	Volume
Date
2000-01-03	^DJI	11357.509766	11357.509766	11522.009766	11305.690430	11501.849609	169750000
2000-01-03	^GSPC	1455.219971	1455.219971	1478.000000	1438.359985	1469.250000	931800000
2000-01-03	^IXIC	4131.149902	4131.149902	4192.189941	3989.709961	4186.189941	1510070000
2000-01-04	^DJI	10997.929688	10997.929688	11350.059570	10986.450195	11349.750000	178420000
2000-01-04	^GSPC	1399.420044	1399.420044	1455.219971	1397.430054	1455.219971	1009000000
...	...	...	...	...	...	...	...
2021-09-20	TQQQ	133.490005	133.490005	137.460007	128.000000	135.419998	60721113
2021-09-20	UDOW	69.760002	69.760002	71.459999	67.519997	70.220001	6439708
2021-09-20	^DJI	33970.468750	33970.468750	34459.718750	33613.031250	34459.718750	441645657
2021-09-20	^GSPC	4357.729980	4357.729980	4402.950195	4305.910156	4402.950195	2598257000
2021-09-20	^IXIC	14713.903320	14713.903320	14841.815430	14530.073242	14758.099609	4350941000

34549 rows × 7 columns

Trade Assets - Quandal

Download the following data from Quandal:

1. CHRIS/CME_S2
2. CHRIS/ICE_CT1

3. CHRIS/CME_SP1

   Where CHRIS is the Quandal Data Provider, and the name after the / is the Futures symbol.

   The format of the JSON to download from Quandal is:

   { quandal-path :

      [ description of data, 
        [ columns to extract from the returned Quandal DataFrame ], 
        { "Quandal_Column" : "Rename Quandal Column" } 
      ]
   

   }

Note:

1. The nested dictionary to rename the original downloaded Quandal data, it is provided since Quandal does
   not have standard output column names.  
       - Futures data from different data sources might have different returned column names for the same
         state, that is "Close" vs. "close", etc.
       - For sharing the same market data manager object it is recommended to rename the columns to have 
         standard names, and match the ones provided by Yahoo Finance

2. Yahoo Finance returns standard equity column names

3. All implemented AI and Technical Models used standard column names (same as Yahoo Finance)
   Columns: Adj Close, Close, Open, High, Low, Volume
   Index: Datetime

4. In order to use the implemented models the Quandal Market Data or any Custom Market Data must have
   similar named columns, or the user can implement his own models and use whatever column names he/she
   prefers.

5. In this example, the Quandal data source does not provide all standard column names, only open,
   which is re-named to 'Adj Close'.  Therefore only some of the built models will be available,
   as they required High, Low, such as Bollinger Bands

indexQuantCodeMap = { 
       "CHRIS/CME_S2"  : ["SoyBeans", ["Open", "Volume"],  { "Open" : "Adj Close" } ],
       "CHRIS/ICE_CT1" : ["Cotton",   ["Open", "Volume"],  { "Open" : "Adj Close" } ],
       "CHRIS/CME_SP1" : ["S&P_500",  ["Open", "Volume"],  { "Open" : "Adj Close" } ]  }

Market Data Manager - Quandal

dirPathMarketDataQuandl = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/TSMarketDat'

beg_date_for_feature_creation = "2000-10-01"
beg_date_str                  = "2001-01-01"
end_date_str                  = "2010-12-31"

q_stock_index_list = list ( indexQuantCodeMap.keys()  )

ts_marketdat_mng_quandal = TradeStrategyMarketDataMng(\
        marketDataProviderName = "quandl",
        supportedTradeAssets   = q_stock_index_list,   
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "Quandl_Test_1_PorfolioRegisterItem_Portf_J",
        dirPathMarketData      = dirPathMarketDataQuandl,
        beg_date_str           = beg_date_for_feature_creation,
        end_date_str           = end_date_str,
        interval               = "1d" ,
        updateDataPassEndDate  = False,
        forceCreateMarketData  = False,
        indexQuantCodeMap      = indexQuantCodeMap     # required for quandl market data provider, defined above
        ) 

2021-10-28 22:15:20,626 [INFO] root: Found previous Market data previously loaded to specified file and dir.

ts_marketdat_mng_quandal.marketDataObj.getDataFrame()

	Index	Index_Name	Provider	Adj Close	Volume
Date
2000-10-02	CHRIS/CME_S2	SoyBeans	Quandl	498.00	4183.0
2000-10-02	CHRIS/CME_SP1	S&P_500	Quandl	1460.50	61706.0
2000-10-02	CHRIS/ICE_CT1	Cotton	Quandl	62.17	40.0
2000-10-03	CHRIS/CME_S2	SoyBeans	Quandl	499.00	4324.0
2000-10-03	CHRIS/CME_SP1	S&P_500	Quandl	1463.00	72987.0
...	...	...	...	...	...
2010-12-30	CHRIS/CME_SP1	S&P_500	Quandl	1255.70	5876.0
2010-12-30	CHRIS/ICE_CT1	Cotton	Quandl	140.00	10309.0
2010-12-31	CHRIS/CME_S2	SoyBeans	Quandl	1387.00	60405.0
2010-12-31	CHRIS/CME_SP1	S&P_500	Quandl	1254.50	10650.0
2010-12-31	CHRIS/ICE_CT1	Cotton	Quandl	144.00	5421.0

7540 rows × 5 columns

Pipeline Data Managers

For AI models, shared pipeline

For Yahoo-Finance Mkt Data

ts_model_pipline_mng_yf = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng_yf,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_YF",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

For User-Defined DataFrame Mkt Data

ts_model_pipline_mng_df = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng_df,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_DF",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

For Quandal Mkt Data

Note:

- The rnn pipeline builder is built explicitily and the columns to use in the AI RNN based models defined.
- The Quandal data source in the example only has data for the 'Adj Close', 'Volume', the default is 
  to assume all standard columns for equities returned by Yahoo Finance

ai_rnn_pipeline_builder = WaveNet_RNN_AI_PipelineBuilder(\
                                        stock_index_col = 'Index',
                                        pipeline_cols   = ['Adj Close', 'Volume' ],
                                        y_feature_col   =  'Adj Close' )

ts_model_pipline_mng_quandal = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng_quandal,
                            modelPipelineBuilder = ai_rnn_pipeline_builder,
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_Quandal",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

Define Multiple SMA Models, LSTM, GRU, MACD, and RSI Models

taRegisterItems = [ ]
tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_1",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 20, 'win_slow' : 100 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_3",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 10, 'win_slow' : 50 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #4

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_4",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_5",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 75 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #6

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_6",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 7, 'win_slow' : 35 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 2 } },
                             },
                    }  )

# TS #7

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_7",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 7, 'win_slow' : 35 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : True },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #8

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #9

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #10

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_macd",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "macd",
                                  'type'     : "ta",
                                  'obj_args' : { 'window_slow' : 26, 'window_fast' : 12, 'window_sign' : 9,
                                                 'enter_long_macd_diff'  : 0.5 , 'exi_long_macd_diff' : 0.5,
                                                 'enter_short_macd_diff' : -0.5, 'exi_short_macd_diff' : -0.5  },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )
# TS #11

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_rsi",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "rsi",
                                  'type'     : "ta",
                                  'obj_args' : { 'rsi_enter_long'  : 30.0,  'rsi_exit_long'  : 30.0, 
                                                 'rsi_enter_short' : 70.0,  'rsi_exit_short' : 70.0 },
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )

Equity Trade Strategies (using Yahoo Finance Market Data)

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng_yf, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_yf )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng_yf, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_yf )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng_yf,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

Futures Trade Strategies (using Quandal Futures data)

tsActionGen = TradeStrategyActionGenerator()

for trade_asset in q_stock_index_list :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng_quandal, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_quandal )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng_quandal, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_quandal )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng_quandal,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

Trade Strategies (using User-Defined Pandas DataFrame)

tsActionGen = TradeStrategyActionGenerator()

for trade_asset in [ '^DJI', '^IXIC' ] :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng_df, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_df )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng_df, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_df )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng_df,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  96

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager, Quandal, YahooFinance, DF",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio Inv. Manager, Quandal, YahooFinance, DF",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 22:16:33,747 [INFO] root: *** Adding long only trade strategies for : {'CHRIS/CME_SP1', 'CHRIS/ICE_CT1', '^GSPC', '^DJI', '^IXIC', 'CHRIS/CME_S2'}

Persist to DB (optional)

session.add( portf_registered_obj )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Re-Store from DB (optional)

session.expunge_all()
del portf_registered_obj

for tsObj in session.query(PortfolioRegisterItem):  
    print(tsObj.name)
    if  tsObj.name  == "Portfolio Inv. Manager, Quandal, YahooFinance, DF" :
        break

portf_registered_obj = tsObj
print(portf_registered_obj.id, portf_registered_obj.name)

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2000-10-05",
                             strBegDate         = "2001-01-01",
                             strEndDate         = "2006-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2006-07-01",
                               strBegDate         = "2007-01-01",
                               strEndDate         = "2008-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2008-07-01",
                               strBegDate         = "2009-01-01",
                               strEndDate         = "2009-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Backtest

tsExecuteEngine = BacktestExecution( \
                    name = "Backtest for Portfolio J",
                    timePeriod  = testing_period,
                    fitDatasets = fit_datasets,
                    trade_asset_registered_item = portf_registered_obj )

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("CHRIS")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
37	ts_sma_1_CHRIS/CME_S2	No	5	1.941176e+06	1.181225e+06	-0.391490	Completed
38	ts_sma_2_CHRIS/CME_S2	No	6	1.941176e+06	1.782112e+06	-0.081942	Completed
39	ts_sma_3_CHRIS/CME_S2	No	6	1.941176e+06	1.781919e+06	-0.082041	Completed
40	ts_sma_4_CHRIS/CME_S2	No	5	1.941176e+06	1.642411e+06	-0.153909	Completed
41	ts_sma_5_CHRIS/CME_S2	No	5	1.941176e+06	1.255283e+06	-0.353339	Completed
42	ts_sma_6_CHRIS/CME_S2	No	7	1.941176e+06	1.915940e+06	-0.013000	Completed
43	ts_sma_7_CHRIS/CME_S2	No	7	1.941176e+06	1.798736e+06	-0.073378	Completed
44	ts_gru_CHRIS/CME_S2	No	93	1.941176e+06	1.837204e+06	-0.053561	Completed
45	ts_lstm_CHRIS/CME_S2	No	64	1.941176e+06	1.910883e+06	-0.015605	Completed
46	ts_macd_CHRIS/CME_S2	No	22	1.941176e+06	1.931941e+06	-0.004757	Completed
47	ts_rsi_CHRIS/CME_S2	No	9	1.941176e+06	1.909357e+06	-0.016392	Completed
48	all_CHRIS/CME_S2	No	30	1.941176e+06	1.803234e+06	-0.071061	Completed
49	ts_sma_1_CHRIS/ICE_CT1	No	1	1.941176e+06	3.032058e+06	0.561969	Completed
50	ts_sma_2_CHRIS/ICE_CT1	No	7	1.941176e+06	1.411244e+06	-0.272995	Completed
51	ts_sma_3_CHRIS/ICE_CT1	No	7	1.941176e+06	1.654444e+06	-0.147711	Completed
52	ts_sma_4_CHRIS/ICE_CT1	No	5	1.941176e+06	2.200841e+06	0.133766	Completed
53	ts_sma_5_CHRIS/ICE_CT1	No	5	1.941176e+06	2.283220e+06	0.176204	Completed
54	ts_sma_6_CHRIS/ICE_CT1	No	6	1.941176e+06	1.759581e+06	-0.093549	Completed
55	ts_sma_7_CHRIS/ICE_CT1	No	6	1.941176e+06	2.119280e+06	0.091750	Completed
56	ts_gru_CHRIS/ICE_CT1	No	69	1.941176e+06	2.725994e+06	0.404300	Completed
57	ts_lstm_CHRIS/ICE_CT1	No	47	1.941176e+06	2.135238e+06	0.099971	Completed
58	ts_macd_CHRIS/ICE_CT1	No	24	1.941176e+06	1.695369e+06	-0.126628	Completed
59	ts_rsi_CHRIS/ICE_CT1	No	7	1.941176e+06	2.116962e+06	0.090556	Completed
60	all_CHRIS/ICE_CT1	No	22	1.941176e+06	2.342335e+06	0.206657	Completed
61	ts_sma_1_CHRIS/CME_SP1	No	1	1.941176e+06	2.600468e+06	0.339635	Completed
62	ts_sma_2_CHRIS/CME_SP1	No	5	1.941176e+06	2.261261e+06	0.164892	Completed
63	ts_sma_3_CHRIS/CME_SP1	No	5	1.941176e+06	2.147903e+06	0.106495	Completed
64	ts_sma_4_CHRIS/CME_SP1	No	3	1.941176e+06	2.358685e+06	0.215080	Completed
65	ts_sma_5_CHRIS/CME_SP1	No	1	1.941176e+06	2.597068e+06	0.337883	Completed
66	ts_sma_6_CHRIS/CME_SP1	No	6	1.941176e+06	2.278610e+06	0.173829	Completed
67	ts_sma_7_CHRIS/CME_SP1	No	6	1.941176e+06	2.224219e+06	0.145810	Completed
68	ts_gru_CHRIS/CME_SP1	No	65	1.941176e+06	2.049154e+06	0.055625	Completed
69	ts_lstm_CHRIS/CME_SP1	No	32	1.941176e+06	2.288071e+06	0.178703	Completed
70	ts_macd_CHRIS/CME_SP1	No	31	1.941176e+06	1.634286e+06	-0.158095	Completed
71	ts_rsi_CHRIS/CME_SP1	No	11	1.941176e+06	2.157958e+06	0.111675	Completed
72	all_CHRIS/CME_SP1	No	22	1.941176e+06	2.284183e+06	0.176700	Completed
97	Long-Only Trade Strategy for CHRIS/CME_SP1	No	1	1.941176e+06	2.422208e+06	0.247804	Completed
98	Long-Only Trade Strategy for CHRIS/ICE_CT1	No	1	1.941176e+06	2.975400e+06	0.532782	Completed
102	Long-Only Trade Strategy for CHRIS/CME_S2	No	1	1.941176e+06	2.101256e+06	0.082465	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio Inv. Manager, Quandal, YahooFinance, DF	Yes			2009-01-05	2009-12-31	1.0	0.12	0.12	1.19	-0.04	0.02	-0.02
1	ts_sma_1_^DJI	No	ts_sma_1	^DJI	2009-01-05	2009-12-31	0.68	0.28	0.28	1.83	-0.07	0.03	-0.03
2	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2009-01-05	2009-12-31	0.99	0.13	0.13	0.67	-0.15	0.05	-0.06
3	ts_sma_3_^DJI	No	ts_sma_3	^DJI	2009-01-05	2009-12-31	0.98	0.18	0.18	0.86	-0.15	0.05	-0.06
4	ts_sma_4_^DJI	No	ts_sma_4	^DJI	2009-01-05	2009-12-31	0.75	0.07	0.07	0.49	-0.18	0.03	-0.04
...	...	...	...	...	...	...	...	...	...	...	...	...	...
98	Long-Only Trade Strategy for CHRIS/ICE_CT1	No	long	CHRIS/ICE_CT1	2009-01-05	2009-12-31	1.0	0.53	0.54	1.47	-0.23	0.12	-0.07
99	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
100	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2009-01-05	2009-12-31	1.0	0.15	0.16	0.72	-0.27	0.07	-0.05
101	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2009-01-05	2009-12-31	1.0	0.39	0.4	1.32	-0.23	0.07	-0.06
102	Long-Only Trade Strategy for CHRIS/CME_S2	No	long	CHRIS/CME_S2	2009-01-05	2009-12-31	0.98	0.08	0.08	0.41	-0.25	0.06	-0.08

103 rows × 13 columns

tsExecuteEngine.getSummaryMetrics().T.head(60)

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

WFA Backtest

A type of backtests that are designed to simulate fitting AI Models to different date ranges or to shift the date ranges for a backtest through time.

- Description :

- Re-fit the AI models that a given trade strategy depends on during a backtest

- Scenario:

- Given the following initial setting for a backtest:

    AI Models Fit Date Ranges
        1. Training Period: 
            1. Begin Date = 2001-01-01 
            2. End Date   = 2005-12-31  
        2. Validation Period: 
            1. Begin Date = 2006-01-01 
            2. End Date   = 2007-06-30   

    Test Period: 
        1. Begin Date = 2008-01-01 
        2. End Date   = 2008-12-31 

- Re-fit the models after the backtest has executed for a period (n days, n weeks, n months), by 
  either shifting the Training and Validation period or extending, that is anchoring the Training
  Period start date

Example 1

• shifting Training and Validation periods
• re-fit every three months
• the AI Models will be trained and validate with the following date ranges:
  • Iteration 1:

    1. Training Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2005-12-31  
    2. Validation Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2007-06-30   

  • Iteration 2:

    1. Training Period: 
        1. Begin Date = 2001-04-01 
        2. End Date   = 2006-03-31  
    2. Validation Period: 
        1. Begin Date = 2006-04-01 
        2. End Date   = 2007-09-30    
    

    • Iteration n: ...
• The backtest, for dates between 2008-04-01 and 2008-06-30, will use the updated re-fitted models
• The re-fit process will continue throughout the backtest until the backtest ends.

Example 2

• anchored Training Set
• re-fit every three months
• the AI Models will be trained and validate with the following date ranges:
  • Iteration 1:

    1. Training Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2005-12-31  
    2. Validation Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2007-06-30  

  • Iteration 2:

    1. Training Period: 
        1. Begin Date = 2001-01-01 
        2. End Date   = 2006-03-31  
    2. Validation Period: 
        1. Begin Date = 2006-04-01 
        2. End Date   = 2007-09-30 

  • Iteration n:

    ... 

• The backtest, for dates between 2008-04-01 and 2008-06-30, will use the updated re-fitted models
• The re-fit process will continue throughout the backtest

WFA-Backtest 1

Description:

• Portfolio has a subset of the trade strategies in Portfolio D
  • Smaller subset of AI models to reduce execution duration
• Re-fit AI trading models every 4 months. For this example, there will be a total of three times the AI models are fitted, that is:
  1. Before WFA Backtest starts
  2. May 1
  3. September 1
• Training-set is not anchored

Backtest:

1. Type: WFA Backtest
2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2000-01-01 
        2. End Date   = 2005-12-31  
    2. Validation Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2007-12-31   
4. Test Period: 
    1. Begin Date = 2008-01-01 
    2. End Date   = 2008-12-31         

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "1980-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_From_1980",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

******* Initial Market Data Download ******* 

Downloading: ^DJI interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: ^IXIC interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: ^GSPC interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: UDOW interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: SDOW interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: SPXL interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: SPXS interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: TQQQ interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed
Downloading: SQQQ interval = 1d  beg_date = 1980-01-01  end_date = None
[*********************100%***********************]  1 of 1 completed

MarketData.getDateRangesForStocks( ts_marketdat_mng.marketDataObj.getDataFrame() )

	Index	Beg_Date	End_Date
0	^GSPC	1980-01-02	2021-10-28
1	^IXIC	1980-01-02	2021-10-28
2	^DJI	1992-01-02	2021-10-28
3	SPXL	2008-11-05	2021-10-28
4	SPXS	2008-11-19	2021-10-28
5	SDOW	2010-02-11	2021-10-28
6	SQQQ	2010-02-11	2021-10-28
7	TQQQ	2010-02-11	2021-10-28
8	UDOW	2010-02-11	2021-10-28

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_1",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_3",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_5",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 40, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = None )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = None )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  12

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Multiple-LSTM  TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with Multiple-LSTM and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-28 23:13:41,108 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "1999-07-01",
                             strBegDate         = "2000-01-01",
                             strEndDate         = "2005-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2008-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute WFA Backtest

tsExecuteEngine = BacktestExecutionWFA( \
            name                        = "WFA Backtest for subset of Portfolio D - non-anchored training",
            test_period                 = testing_period,
            fit_datasets_start          = fit_datasets,
            trade_asset_registered_item = portf_registered_obj,
            wfa_slide_win_size          = 4,
            wfa_slide_win_period        = WFASlideWindowPeriod.Months, 
            anchor_fit_dataset          = False )                      

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']]

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
0	Portfolio of US-Indexes with Multiple-LSTM and...	Yes	641	30000000.0	4.696890e+07	0.565630	Completed
1	ts_lstm_1_^DJI	No	64	2000000.0	4.732744e+06	1.366372	Completed
2	ts_lstm_3_^DJI	No	52	2000000.0	4.159925e+06	1.079962	Completed
3	ts_lstm_5_^DJI	No	70	2000000.0	4.870862e+06	1.435431	Completed
4	all_^DJI	No	62	2000000.0	4.309855e+06	1.154927	Completed
5	ts_lstm_1_^IXIC	No	78	2000000.0	3.716019e+06	0.858009	Completed
6	ts_lstm_3_^IXIC	No	76	2000000.0	5.669807e+06	1.834904	Completed
7	ts_lstm_5_^IXIC	No	90	2000000.0	4.108412e+06	1.054206	Completed
8	all_^IXIC	No	80	2000000.0	4.039707e+06	1.019853	Completed
9	ts_lstm_1_^GSPC	No	22	2000000.0	2.463057e+06	0.231529	Completed
10	ts_lstm_3_^GSPC	No	6	2000000.0	1.435241e+06	-0.282379	Completed
11	ts_lstm_5_^GSPC	No	18	2000000.0	1.737240e+06	-0.131380	Completed
12	all_^GSPC	No	20	2000000.0	1.921625e+06	-0.039187	Completed
13	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	1.248316e+06	-0.375842	Completed
14	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	1.209029e+06	-0.395486	Completed
15	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	1.347062e+06	-0.326469	Completed

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2008-01-03	2008-12-31	1.0	0.57	0.57	1.59	-0.18	0.11	-0.07
1	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2008-01-03	2008-12-31	1.0	1.37	1.38	2.46	-0.19	0.11	-0.07
2	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2008-01-03	2008-12-31	1.0	1.08	1.09	2.12	-0.19	0.11	-0.07
3	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2008-01-03	2008-12-31	1.0	1.44	1.45	2.53	-0.19	0.11	-0.07
4	all_^DJI	No	all	^DJI	2008-01-03	2008-12-31	1.0	1.15	1.16	2.2	-0.19	0.11	-0.07
5	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2008-01-03	2008-12-31	1.0	0.86	0.86	1.68	-0.24	0.12	-0.08
6	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2008-01-03	2008-12-31	1.0	1.83	1.85	2.75	-0.18	0.12	-0.07
7	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2008-01-03	2008-12-31	1.0	1.05	1.06	1.95	-0.21	0.12	-0.08
8	all_^IXIC	No	all	^IXIC	2008-01-03	2008-12-31	1.0	1.02	1.03	1.89	-0.24	0.12	-0.08
9	ts_lstm_1_^GSPC	No	ts_lstm_1	^GSPC	2008-01-03	2008-12-31	1.0	0.23	0.23	0.71	-0.27	0.12	-0.09
10	ts_lstm_3_^GSPC	No	ts_lstm_3	^GSPC	2008-01-03	2008-12-31	1.0	-0.28	-0.28	-0.6	-0.46	0.12	-0.09
11	ts_lstm_5_^GSPC	No	ts_lstm_5	^GSPC	2008-01-03	2008-12-31	1.0	-0.13	-0.13	-0.14	-0.3	0.12	-0.09
12	all_^GSPC	No	all	^GSPC	2008-01-03	2008-12-31	1.0	-0.04	-0.04	0.11	-0.3	0.12	-0.09
13	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2008-01-03	2008-12-31	1.0	-0.38	-0.38	-0.94	-0.48	0.12	-0.09
14	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2008-12-31	1.0	-0.4	-0.4	-1.02	-0.49	0.12	-0.09
15	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2008-12-31	1.0	-0.33	-0.33	-0.86	-0.42	0.11	-0.08

tsExecuteEngine.getSummaryMetrics().T.head(60)

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
ta_reg_item_name	Portfolio of US-Indexes with Multiple-LSTM and...	ts_lstm_1_^DJI	ts_lstm_3_^DJI	ts_lstm_5_^DJI	all_^DJI	ts_lstm_1_^IXIC	ts_lstm_3_^IXIC	ts_lstm_5_^IXIC	all_^IXIC	ts_lstm_1_^GSPC	ts_lstm_3_^GSPC	ts_lstm_5_^GSPC	all_^GSPC	Long-Only Trade Strategy for ^GSPC	Long-Only Trade Strategy for ^IXIC	Long-Only Trade Strategy for ^DJI
ta_reg_item_id	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None
is_portfolio	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No
model_type		ts_lstm_1	ts_lstm_3	ts_lstm_5	all	ts_lstm_1	ts_lstm_3	ts_lstm_5	all	ts_lstm_1	ts_lstm_3	ts_lstm_5	all	long	long	long
trade_asset		^DJI	^DJI	^DJI	^DJI	^IXIC	^IXIC	^IXIC	^IXIC	^GSPC	^GSPC	^GSPC	^GSPC	^GSPC	^IXIC	^DJI
test_period_len_days	365	365	365	365	365	365	365	365	365	365	365	365	365	365	365	365
tr_period_len_days	2191	2191	2191	2191	2191	2191	2191	2191	2191	2191	2191	2191	2191	2191	2191	2191
val_period_len_days	729	729	729	729	729	729	729	729	729	729	729	729	729	729	729	729
index	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy	Strategy
Start Period	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03	2008-01-03
End Period	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31	2008-12-31
Risk-Free Rate	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Time in Market	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
Cumulative Return	0.57	1.37	1.08	1.44	1.15	0.86	1.83	1.05	1.02	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
CAGR%	0.57	1.38	1.09	1.45	1.16	0.86	1.85	1.06	1.03	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
Sharpe	1.59	2.46	2.12	2.53	2.2	1.68	2.75	1.95	1.89	0.71	-0.6	-0.14	0.11	-0.94	-1.02	-0.86
Sortino	2.7	4.44	3.75	4.48	3.86	2.76	4.99	3.2	3.11	1.12	-0.83	-0.2	0.16	-1.29	-1.4	-1.2
Sortino/√2	1.91	3.14	2.65	3.17	2.73	1.95	3.53	2.26	2.2	0.79	-0.59	-0.14	0.11	-0.91	-0.99	-0.85
Max Drawdown	-0.18	-0.19	-0.19	-0.19	-0.19	-0.24	-0.18	-0.21	-0.24	-0.27	-0.46	-0.3	-0.3	-0.48	-0.49	-0.42
Longest DD Days	74	75	57	48	48	113	63	64	74	122	225	362	225	362	362	240
Volatility (ann.)	0.31	0.38	0.38	0.38	0.38	0.42	0.41	0.41	0.42	0.41	0.41	0.41	0.41	0.41	0.41	0.38
Calmar	3.13	7.13	5.63	7.47	6.01	3.56	10.55	5.18	4.23	0.87	-0.62	-0.44	-0.13	-0.79	-0.8	-0.78
Skew	1.17	0.93	0.9	0.84	0.87	0.64	0.8	0.5	0.6	0.72	0.14	0.45	0.41	0.18	0.21	0.43
Kurtosis	8.5	3.88	3.75	3.94	3.93	3.25	2.77	2.91	3.27	3.9	3.96	3.89	3.87	3.99	3.22	4.22
Expected Daily %	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0
Expected Monthly %	0.04	0.07	0.06	0.08	0.07	0.05	0.09	0.06	0.06	0.02	-0.03	-0.01	-0.0	-0.04	-0.04	-0.03
Expected Yearly %	0.57	1.37	1.08	1.44	1.15	0.86	1.83	1.05	1.02	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
Kelly Criterion	0.16	0.21	0.18	0.22	0.19	0.14	0.22	0.17	0.16	0.07	-0.06	-0.01	0.01	-0.1	-0.09	-0.08
Risk of Ruin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Daily Value-at-Risk	-0.03	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Expected Shortfall (cVaR)	-0.03	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04	-0.04
Gain/Pain Ratio	0.42	0.58	0.48	0.61	0.51	0.36	0.65	0.42	0.41	0.14	-0.11	-0.03	0.02	-0.16	-0.17	-0.15
Gain/Pain (1M)	4.28	8.85	10.56	11.3	9.87	2.15	16.07	5.68	3.06	1.02	-0.6	-0.17	0.15	-0.81	-0.69	-0.82
Payoff Ratio	1.23	1.25	1.25	1.19	1.19	1.12	1.3	1.12	1.11	1.03	0.83	0.92	0.92	0.83	0.96	0.92
Profit Factor	1.42	1.58	1.48	1.61	1.51	1.36	1.65	1.42	1.41	1.14	0.89	0.97	1.02	0.84	0.83	0.85
Common Sense Ratio	2.07	2.09	2.04	1.9	1.78	1.63	2.35	1.64	1.59	1.32	0.81	0.98	1.03	0.76	0.77	0.74
CPC Index	0.94	1.1	1.0	1.1	1.0	0.83	1.19	0.89	0.88	0.62	0.38	0.46	0.49	0.35	0.37	0.38
Tail Ratio	1.46	1.32	1.37	1.18	1.18	1.2	1.43	1.15	1.13	1.16	0.91	1.01	1.01	0.91	0.92	0.86
Outlier Win Ratio	5.11	4.74	4.49	4.85	4.83	4.85	4.58	4.71	4.83	5.34	4.24	4.75	4.75	4.26	3.61	4.23
Outlier Loss Ratio	5.56	3.52	3.69	3.59	3.58	3.49	3.38	3.53	3.49	3.71	4.29	3.93	3.92	4.3	4.04	4.23
MTD	0.27	0.32	0.24	0.27	0.32	0.43	0.37	0.34	0.37	0.4	0.01	0.13	0.16	0.01	0.03	-0.01
3M	0.23	0.59	0.37	0.37	0.42	0.2	0.59	0.22	0.15	0.2	-0.17	-0.03	-0.0	-0.18	-0.2	-0.15
6M	0.49	1.35	0.92	0.96	1.03	0.75	1.15	0.64	0.6	0.32	-0.28	-0.03	-0.0	-0.29	-0.31	-0.23
YTD	0.57	1.37	1.08	1.44	1.15	0.86	1.83	1.05	1.02	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
1Y	0.57	1.37	1.08	1.44	1.15	0.86	1.83	1.05	1.02	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
3Y (ann.)	0.57	1.38	1.09	1.45	1.16	0.86	1.85	1.06	1.03	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
5Y (ann.)	0.57	1.38	1.09	1.45	1.16	0.86	1.85	1.06	1.03	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
10Y (ann.)	0.57	1.38	1.09	1.45	1.16	0.86	1.85	1.06	1.03	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
All-time (ann.)	0.57	1.38	1.09	1.45	1.16	0.86	1.85	1.06	1.03	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
Best Day	0.11	0.11	0.11	0.11	0.11	0.12	0.12	0.12	0.12	0.12	0.12	0.12	0.12	0.12	0.12	0.11
Worst Day	-0.07	-0.07	-0.07	-0.07	-0.07	-0.08	-0.07	-0.08	-0.08	-0.09	-0.09	-0.09	-0.09	-0.09	-0.09	-0.08
Best Month	0.27	0.32	0.28	0.27	0.32	0.43	0.37	0.34	0.37	0.4	0.07	0.13	0.16	0.05	0.06	0.05
Worst Month	-0.09	-0.08	-0.03	-0.08	-0.08	-0.14	-0.05	-0.09	-0.14	-0.14	-0.15	-0.14	-0.14	-0.17	-0.18	-0.14
Best Year	0.57	1.37	1.08	1.44	1.15	0.86	1.83	1.05	1.02	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
Worst Year	0.57	1.37	1.08	1.44	1.15	0.86	1.83	1.05	1.02	0.23	-0.28	-0.13	-0.04	-0.38	-0.4	-0.33
Avg. Drawdown	-0.03	-0.05	-0.04	-0.04	-0.04	-0.06	-0.03	-0.04	-0.04	-0.06	-0.07	-0.3	-0.06	-0.48	-0.49	-0.26
Avg. Drawdown Days	11	15	12	10	11	26	9	13	14	27	38	362	34	362	362	180
Recovery Factor	3.11	7.07	5.59	7.41	5.96	3.53	10.46	5.14	4.2	0.86	-0.61	-0.44	-0.13	-0.78	-0.8	-0.78
Ulcer Index	3.39	1.8	1.79	1.77	1.79	1.47	1.46	1.48	1.46	inf	inf	inf	inf	inf	1.56	1.86
Avg. Up Month	0.07	0.12	0.11	0.11	0.1	0.16	0.14	0.11	0.14	0.1	0.03	0.04	0.04	0.02	0.03	0.02

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

WFA-Backtest 2

Description:

• Portfolio has a subset of the trade strategies in Portfolio G
  • Smaller subset of TA and AI models to reduce execution duration
• Re-fit AI trading models every 6 months
• Training-set is anchored

Backtest:

1. Type: WFA Backtest
2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2000-01-01 
        2. End Date   = 2005-12-31  
    2. Validation Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2007-12-31   
4. Test Period: 
    1. Begin Date = 2008-01-01 
    2. End Date   = 2010-12-31  

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "1980-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_From_1980",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-29 09:33:42,132 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

ts_model_pipline_mng_g = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_G",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_1", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )
# TS #3
    
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'   : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #8
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_2", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'          : 40,   'no_neurons_lay1'    : 20,
                                         'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                         'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                         'epochs'          : 20 }  
                                }
                            } }  )

# TS #10

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )

# TS #16

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_kama",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "kama",
                              'type'     : "ta",
                              'obj_args' : { 'window_fast' : 10, 'pow1_fast' : 2, 'pow2_fast'  : 30,
                                             'window_slow' : 10, 'pow1_slow' : 5, 'pow2_slow'  : 30 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #17

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_tsi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "tsi",
                              'type'     : "ta",
                              'obj_args' : { 'window_slow' : 25, 'window_fast' : 5 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_g )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_g )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  24

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - TechAnal & AI Trading Models TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with TechAnal & AI Trading Models, and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-29 09:34:14,401 [INFO] root: *** Adding long only trade strategies for : {'^GSPC', '^IXIC', '^DJI'}

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "1999-07-01",
                             strBegDate         = "2000-01-01",
                             strEndDate         = "2005-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2010-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute WFA Backtest

tsExecuteEngine = BacktestExecutionWFA( \
            name                        = "WFA Backtest for subset of Portfolio G - anchored training-set",
            test_period                 = testing_period,
            fit_datasets_start          = fit_datasets,
            trade_asset_registered_item = portf_registered_obj,
            wfa_slide_win_size          = 6,
            wfa_slide_win_period        = WFASlideWindowPeriod.Months, 
            anchor_fit_dataset          = True )                      

tsExecuteEngine.evaluate()

Backtest Metrics

Trade Strategies Execution & Investment Metrics

DOW Jones

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("DJI")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
1	ts_gru_1_^DJI	No	237	2000000.0	4.702350e+06	1.351175	Completed
2	ts_lstm_^DJI	No	195	2000000.0	4.397900e+06	1.198950	Completed
3	ts_conv1d_gru_^DJI	No	192	2000000.0	5.047832e+06	1.523916	Completed
4	ts_gru_2_^DJI	No	303	2000000.0	5.728731e+06	1.864365	Completed
5	ts_sma_2_^DJI	No	12	2000000.0	2.659531e+06	0.329765	Completed
6	ts_kama_^DJI	No	9	2000000.0	2.879268e+06	0.439634	Completed
7	ts_tsi_^DJI	No	33	2000000.0	2.542929e+06	0.271465	Completed
8	all_^DJI	No	239	2000000.0	5.629759e+06	1.814880	Completed
27	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	1.775633e+06	-0.112183	Completed

Nasdaq

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("IXIC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
9	ts_gru_1_^IXIC	No	273	2000000.0	2.892646e+06	0.446323	Completed
10	ts_lstm_^IXIC	No	259	2000000.0	5.371504e+06	1.685752	Completed
11	ts_conv1d_gru_^IXIC	No	181	2000000.0	5.940391e+06	1.970195	Completed
12	ts_gru_2_^IXIC	No	289	2000000.0	5.161725e+06	1.580863	Completed
13	ts_sma_2_^IXIC	No	16	2000000.0	2.528602e+06	0.264301	Completed
14	ts_kama_^IXIC	No	12	2000000.0	3.229503e+06	0.614752	Completed
15	ts_tsi_^IXIC	No	30	2000000.0	2.700634e+06	0.350317	Completed
16	all_^IXIC	No	238	2000000.0	6.027619e+06	2.013809	Completed
26	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	2.033122e+06	0.016561	Completed

S&P 500

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("GSPC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
17	ts_gru_1_^GSPC	No	241	2000000.0	3.800799e+06	0.900399	Completed
18	ts_lstm_^GSPC	No	93	2000000.0	2.355349e+06	0.177675	Completed
19	ts_conv1d_gru_^GSPC	No	136	2000000.0	3.257254e+06	0.628627	Completed
20	ts_gru_2_^GSPC	No	221	2000000.0	4.945959e+06	1.472980	Completed
21	ts_sma_2_^GSPC	No	15	2000000.0	2.967038e+06	0.483519	Completed
22	ts_kama_^GSPC	No	5	2000000.0	3.511348e+06	0.755674	Completed
23	ts_tsi_^GSPC	No	37	2000000.0	2.262027e+06	0.131013	Completed
24	all_^GSPC	No	149	2000000.0	6.611552e+06	2.305776	Completed
25	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	1.738083e+06	-0.130958	Completed

Performance Metrics

Dow Jones

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^DJI" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
1	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2008-01-03	2010-12-31	1.0	1.35	0.33	1.16	-0.19	0.11	-0.07
2	ts_lstm_^DJI	No	ts_lstm	^DJI	2008-01-03	2010-12-31	1.0	1.2	0.3	1.09	-0.24	0.11	-0.07
3	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2008-01-03	2010-12-31	1.0	1.52	0.36	1.25	-0.24	0.11	-0.07
4	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2008-01-03	2010-12-31	1.0	1.86	0.42	1.4	-0.19	0.11	-0.07
5	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2008-01-03	2010-12-31	0.99	0.33	0.1	0.58	-0.2	0.06	-0.06
6	ts_kama_^DJI	No	ts_kama	^DJI	2008-01-03	2010-12-31	1.0	0.44	0.13	0.74	-0.27	0.05	-0.05
7	ts_tsi_^DJI	No	ts_tsi	^DJI	2008-01-03	2010-12-31	0.96	0.27	0.08	0.47	-0.25	0.07	-0.07
8	all_^DJI	No	all	^DJI	2008-01-03	2010-12-31	1.0	1.81	0.41	1.38	-0.2	0.11	-0.07
27	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2010-12-31	1.0	-0.11	-0.04	-0.01	-0.5	0.11	-0.08

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^DJI" ').T.head(60)

Nasdaq

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^IXIC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
9	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2008-01-03	2010-12-31	1.0	0.45	0.13	0.55	-0.4	0.12	-0.08
10	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2008-01-03	2010-12-31	1.0	1.69	0.39	1.21	-0.24	0.12	-0.07
11	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2008-01-03	2010-12-31	1.0	1.97	0.44	1.32	-0.21	0.12	-0.07
12	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2008-01-03	2010-12-31	1.0	1.58	0.37	1.16	-0.37	0.11	-0.12
13	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2008-01-03	2010-12-31	0.99	0.26	0.08	0.43	-0.31	0.09	-0.07
14	ts_kama_^IXIC	No	ts_kama	^IXIC	2008-01-03	2010-12-31	1.0	0.61	0.17	0.81	-0.22	0.08	-0.07
15	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2008-01-03	2010-12-31	0.96	0.35	0.11	0.52	-0.33	0.08	-0.06
16	all_^IXIC	No	all	^IXIC	2008-01-03	2010-12-31	1.0	2.01	0.45	1.32	-0.26	0.11	-0.12
26	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2010-12-31	1.0	0.02	0.01	0.17	-0.51	0.12	-0.09

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^IXIC" ').T.head(60)

S&P 500

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^GSPC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
17	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2008-01-03	2010-12-31	1.0	0.9	0.24	0.86	-0.25	0.12	-0.09
18	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2008-01-03	2010-12-31	1.0	0.18	0.06	0.33	-0.43	0.12	-0.09
19	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2008-01-03	2010-12-31	1.0	0.63	0.18	0.68	-0.27	0.12	-0.09
20	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2008-01-03	2010-12-31	1.0	1.47	0.35	1.15	-0.25	0.12	-0.09
21	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2008-01-03	2010-12-31	0.98	0.48	0.14	0.68	-0.25	0.07	-0.06
22	ts_kama_^GSPC	No	ts_kama	^GSPC	2008-01-03	2010-12-31	1.0	0.76	0.21	1.04	-0.15	0.06	-0.05
23	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2008-01-03	2010-12-31	0.94	0.13	0.04	0.29	-0.27	0.08	-0.07
24	all_^GSPC	No	all	^GSPC	2008-01-03	2010-12-31	1.0	2.31	0.49	1.46	-0.19	0.12	-0.08
25	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2008-01-03	2010-12-31	1.0	-0.13	-0.05	-0.0	-0.53	0.12	-0.09

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^GSPC" ').T.head(60)

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

WFA-Backtest 3

Description:

• Portfolio has a subset of the trade strategies in Portfolio G
  • Smaller subset of TA and AI models to reduce execution duration
• Re-fit AI trading models every 6 months
• Training-set is not anchored

Backtest:

1. Type: WFA Backtest
2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2003-01-01 
        2. End Date   = 2008-12-31  
    2. Validation Period: 
        1. Begin Date = 2009-01-01 
        2. End Date   = 2010-12-31   
4. Test Period: 
    1. Begin Date = 2011-01-01 
    2. End Date   = 2012-12-31  

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "1980-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_From_1980",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-30 20:31:13,213 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

ts_model_pipline_mng_g = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_G",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_1", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )
# TS #3
    
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'   : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #8
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_2", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'          : 40,   'no_neurons_lay1'    : 20,
                                         'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                         'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                         'epochs'          : 20 }  
                                }
                            } }  )

# TS #10

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )

# TS #16

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_kama",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "kama",
                              'type'     : "ta",
                              'obj_args' : { 'window_fast' : 10, 'pow1_fast' : 2, 'pow2_fast'  : 30,
                                             'window_slow' : 10, 'pow1_slow' : 5, 'pow2_slow'  : 30 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #17

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_tsi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "tsi",
                              'type'     : "ta",
                              'obj_args' : { 'window_slow' : 25, 'window_fast' : 5 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_g )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_g )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  24

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - TechAnal & AI Trading Models TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with TechAnal & AI Trading Models, and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-30 20:31:51,261 [INFO] root: *** Adding long only trade strategies for : {'^IXIC', '^DJI', '^GSPC'}

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2002-07-01",
                             strBegDate         = "2003-01-01",
                             strEndDate         = "2008-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2008-07-01",
                               strBegDate         = "2009-01-01",
                               strEndDate         = "2010-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2010-07-01",
                               strBegDate         = "2011-01-01",
                               strEndDate         = "2012-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute WFA Backtest

tsExecuteEngine = BacktestExecutionWFA( \
            name                        = "WFA Backtest-3 for subset of Portfolio G - anchored training-set",
            test_period                 = testing_period,
            fit_datasets_start          = fit_datasets,
            trade_asset_registered_item = portf_registered_obj,
            wfa_slide_win_size          = 6,
            wfa_slide_win_period        = WFASlideWindowPeriod.Months, 
            anchor_fit_dataset          = False )                      

tsExecuteEngine.evaluate()

Backtest Metrics

Trade Strategies Execution & Investment Metrics

DOW Jones

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("DJI")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
1	ts_gru_1_^DJI	No	165	2000000.0	1.576387e+06	-0.211807	Completed
2	ts_lstm_^DJI	No	93	2000000.0	2.160426e+06	0.080213	Completed
3	ts_conv1d_gru_^DJI	No	105	2000000.0	2.468124e+06	0.234062	Completed
4	ts_gru_2_^DJI	No	179	2000000.0	1.679280e+06	-0.160360	Completed
5	ts_sma_2_^DJI	No	11	2000000.0	1.841726e+06	-0.079137	Completed
6	ts_kama_^DJI	No	8	2000000.0	1.988360e+06	-0.005820	Completed
7	ts_tsi_^DJI	No	26	2000000.0	1.758361e+06	-0.120820	Completed
8	all_^DJI	No	115	2000000.0	2.046718e+06	0.023359	Completed
26	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	2.245110e+06	0.122555	Completed

Nasdaq

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("IXIC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
9	ts_gru_1_^IXIC	No	143	2000000.0	2.138963e+06	0.069482	Completed
10	ts_lstm_^IXIC	No	111	2000000.0	2.104373e+06	0.052187	Completed
11	ts_conv1d_gru_^IXIC	No	101	2000000.0	2.356619e+06	0.178309	Completed
12	ts_gru_2_^IXIC	No	173	2000000.0	1.912101e+06	-0.043949	Completed
13	ts_sma_2_^IXIC	No	16	2000000.0	1.640432e+06	-0.179784	Completed
14	ts_kama_^IXIC	No	11	2000000.0	1.496984e+06	-0.251508	Completed
15	ts_tsi_^IXIC	No	25	2000000.0	1.806895e+06	-0.096552	Completed
16	all_^IXIC	No	137	2000000.0	2.126406e+06	0.063203	Completed
25	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	2.243697e+06	0.121848	Completed

S&P 500

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("GSPC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
17	ts_gru_1_^GSPC	No	163	2000000.0	2.034552e+06	0.017276	Completed
18	ts_lstm_^GSPC	No	115	2000000.0	2.301003e+06	0.150502	Completed
19	ts_conv1d_gru_^GSPC	No	111	2000000.0	2.525937e+06	0.262968	Completed
20	ts_gru_2_^GSPC	No	171	2000000.0	1.743395e+06	-0.128302	Completed
21	ts_sma_2_^GSPC	No	12	2000000.0	1.942915e+06	-0.028543	Completed
22	ts_kama_^GSPC	No	8	2000000.0	1.809392e+06	-0.095304	Completed
23	ts_tsi_^GSPC	No	27	2000000.0	1.653393e+06	-0.173303	Completed
24	all_^GSPC	No	141	2000000.0	2.073681e+06	0.036840	Completed
27	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	2.242591e+06	0.121295	Completed

Performance Metrics

Dow Jones

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^DJI" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
1	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2011-01-04	2012-12-31	1.0	-0.21	-0.11	-0.61	-0.31	0.05	-0.06
2	ts_lstm_^DJI	No	ts_lstm	^DJI	2011-01-04	2012-12-31	1.0	0.08	0.04	0.31	-0.19	0.04	-0.06
3	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2011-01-04	2012-12-31	1.0	0.23	0.11	0.7	-0.17	0.04	-0.06
4	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2011-01-04	2012-12-31	1.0	-0.16	-0.08	-0.43	-0.24	0.05	-0.06
5	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2011-01-04	2012-12-31	0.99	-0.08	-0.04	-0.17	-0.24	0.06	-0.04
6	ts_kama_^DJI	No	ts_kama	^DJI	2011-01-04	2012-12-31	1.0	-0.01	-0.0	0.06	-0.16	0.05	-0.04
7	ts_tsi_^DJI	No	ts_tsi	^DJI	2011-01-04	2012-12-31	0.94	-0.12	-0.06	-0.34	-0.21	0.05	-0.04
8	all_^DJI	No	all	^DJI	2011-01-04	2012-12-31	1.0	0.02	0.01	0.15	-0.18	0.05	-0.06
26	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2011-01-04	2012-12-31	1.0	0.12	0.06	0.43	-0.17	0.04	-0.06

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^DJI" ').T.head(60)

Nasdaq

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^IXIC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
9	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2011-01-04	2012-12-31	1.0	0.07	0.03	0.27	-0.22	0.05	-0.07
10	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2011-01-04	2012-12-31	1.0	0.05	0.03	0.23	-0.18	0.05	-0.07
11	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2011-01-04	2012-12-31	1.0	0.18	0.09	0.5	-0.2	0.05	-0.07
12	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2011-01-04	2012-12-31	1.0	-0.04	-0.02	-0.0	-0.29	0.05	-0.07
13	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2011-01-04	2012-12-31	0.97	-0.18	-0.09	-0.39	-0.37	0.05	-0.07
14	ts_kama_^IXIC	No	ts_kama	^IXIC	2011-01-04	2012-12-31	1.0	-0.25	-0.14	-0.61	-0.31	0.07	-0.05
15	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2011-01-04	2012-12-31	0.95	-0.1	-0.05	-0.17	-0.26	0.06	-0.04
16	all_^IXIC	No	all	^IXIC	2011-01-04	2012-12-31	1.0	0.06	0.03	0.25	-0.26	0.05	-0.07
25	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2011-01-04	2012-12-31	1.0	0.12	0.06	0.38	-0.19	0.05	-0.07

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^IXIC" ').T.head(60)

S&P 500

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^GSPC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
17	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2011-01-04	2012-12-31	1.0	0.02	0.01	0.14	-0.18	0.05	-0.07
18	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2011-01-04	2012-12-31	1.0	0.15	0.07	0.46	-0.15	0.05	-0.07
19	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2011-01-04	2012-12-31	1.0	0.26	0.12	0.71	-0.14	0.05	-0.07
20	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2011-01-04	2012-12-31	1.0	-0.13	-0.07	-0.27	-0.25	0.05	-0.07
21	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2011-01-04	2012-12-31	0.98	-0.03	-0.01	0.01	-0.23	0.07	-0.04
22	ts_kama_^GSPC	No	ts_kama	^GSPC	2011-01-04	2012-12-31	1.0	-0.1	-0.05	-0.2	-0.23	0.06	-0.04
23	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2011-01-04	2012-12-31	0.95	-0.17	-0.09	-0.47	-0.24	0.06	-0.04
24	all_^GSPC	No	all	^GSPC	2011-01-04	2012-12-31	1.0	0.04	0.02	0.19	-0.18	0.05	-0.07
27	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2011-01-04	2012-12-31	1.0	0.12	0.06	0.4	-0.19	0.05	-0.07

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^GSPC" ').T.head(60)

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Slide Window WFA Backtest

- Description :

- Run a set of backtests by shifting the Training, Validation and Test Periods through time, or
  increasing the duration of the Training and Validation Periods through time and shifting only
  the Test Period
- Purpose of these set of backtest is to analysis the out-of-sample performance for trade strategies
  and/or portfolios through time
- The sliding-window of the training, validation, and test periods are configurable.
- This type of backtest is combined with the regular WFA backtest, which allows to re-fit
  models during each sliding window shift per the configuration of the regular WFA Backtest. 
- This type of backtest can be thought of as double sliding window, one sliding window for shifting or 
  increasing the size of the training and validation periods, the second one to control how often
  to re-fit models during a backtest.
- Due to the potential large number of backtests that can be executed, only the metadata, and summary 
  statistics and performance data are stored per backtest. 
      - All backtest statistical and metadata summary information is stored in a dataframe
      - The metadata information stored includes actual Fit Data Ranges, Test Period, and re-fit
        frequency during a backtest

- Example A:

- Scenario 1:
    - Fix Fit and Test Periods.

- Given the following initial setting for a backtest:

    Fit Dataset:   
        train:       1/1/2000 to 12/31/2004
        validation:  1/1/2005 to 12/31/2005

    Test Period:     1/1/2006 to 12/31/2006

- Run WFA backtests with the datasets above for different re-fit set frequencies, non-anchored:
    - 2 weeks
    - 1 month
    - 3 month

- This example will run the WFA defined part 1, three-times, re-fitting the AI models each time
  per the frequency defined.

- Example B:

- Scenario 2:
    - WFA Scenario implementation that slides through time the Fit datasets as well as the test period.

- Given the following initial setting for a backtest:

    Fit Dataset:   
        train:       1/1/2000 to 12/31/2004
        validation:  1/1/2005 to 12/31/2005

    Test Period:     1/1/2006 to 12/31/2006

- Run WFA backtests from the starting datasets above, incrementing sliding window of the datasets 
  for the Fit and Test periods by one-year, while the end date for the test period is less than 
  or equal to 12/31/2020

- Re-fit the AI Models in a backtest every 6 months, don't anchor the training set

- Each Iteration is one complete backtest, and in each backtest the AI Models are fitted at
  the start and at July 1 of the current backtest year.

    Iteration 1:
                    train:       1/1/2000 to 12/31/2004
                    validation:  1/1/2005 to 12/31/2005
                    Test Period: 1/1/2006 to 12/31/2006
    Iteration 2:
                    train:       1/1/2001 to 12/31/2005
                    validation:  1/1/2006 to 12/31/2006
                    Test Period: 1/1/2007 to 12/31/2007   
    ..
    ..
    Iteration Last:
        train:       1/1/2014 to 12/31/2018
        validation:  1/1/2019 to 12/31/2019
        Test Period: 1/1/2020 to 12/31/2020               

- Example C:

- Scenario 3:
    - WFA Scenario implementation that increases through time the date range of the Fit datasets, 
      but not the test period, as the fit dataset is anchored to its initial position. 
    - The train set is anchored, and the train and validation set are expanded in every iteration, 
      pushing the test period dataset forward in time, but with the same length. 
    - The increased per iteration of the train and validation set are configurable, as well as 
      the maximum end date for the test period.

- Given the following initial setting for a backtest:

    Fit Dataset:   
        train:       1/1/2000 to 12/31/2004
        validation:  1/1/2005 to 12/31/2005

    Test Period:     1/1/2006 to 12/31/2006

- Run WFA backtests from the starting datasets above, incrementing sliding window of the datasets 
  for the Fit and Test periods by one-year, while the end date for the test period is less than 
  or equal to 12/31/2020

- Re-fit the AI Models in a backtest every 6 months, don't anchor the training set

- This class will iterate through time by creating updated Fit datsets, and Test Periods, with 
  one year increments overall to the train and validation period. Train dataset increased by 9 months 
  per iteration, and Validation dataset by 3 months, the test period is just shifted by the one-year.
- that is:
    * Iteration 1:
                    train:       1/1/2000 to 12/31/2004
                    validation:  1/1/2005 to 12/31/2005
                    Test Period: 1/1/2006 to 12/31/2006
    * Iteration 2:
                    train:        1/1/2000  to  9/30/2005    (date range increased by nine-months)
                    validation:  10/1/2005  to 12/31/2006    (date range increaed by three-months and 
                                                              shifted by nine-months)
                    Test Period:  1/1/2007  to 12/31/2007    (date range shifted by one-year)
    * Iteration 3:
                    train:        1/1/2000  to  6/30/2006    (date range increased by nine-months)
                    validation:   7/1/2006  to 12/31/2007    (date range increaed by three-months and 
                                                              shifted by nine-months)
                    Test Period:  1/1/2008  to 12/31/2008    (date range shifted by one-year)
    ..
    ..

    * Iteration Last:
        train:       1/1/2000 to xxx
        validation:  yyy      to 12/31/2019       
        Test Period: 1/1/2020 to 12/31/2020                        

SW-WFA-Backtest 1

Description:

• Portfolio has a subset of the trade strategies in Portfolio D
  • Smaller subset of AI models to reduce execution duration
• Re-fit AI trading models every 3 months, then every 6 months within a backtest
• Fixed Training and Validation periods
• Training-set is not anchored during a backtest

Backtest:

1. Type: Slide Window WFA Backtest

2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2000-01-01 
        2. End Date   = 2005-12-31  
    2. Validation Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2007-12-31 

3. Test Period: 
    1. Begin Date = 2008-01-01 
    2. End Date   = 2008-12-31          

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "1980-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_From_1980",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-29 18:22:54,818 [INFO] root: Found previous Market data previously loaded to specified file and dir.

MarketData.getDateRangesForStocks( ts_marketdat_mng.marketDataObj.getDataFrame() )

	Index	Beg_Date	End_Date
0	^GSPC	1980-01-02	2021-10-28
1	^IXIC	1980-01-02	2021-10-28
2	^DJI	1992-01-02	2021-10-28
3	SPXL	2008-11-05	2021-10-28
4	SPXS	2008-11-19	2021-10-28
5	SDOW	2010-02-11	2021-10-28
6	SQQQ	2010-02-11	2021-10-28
7	TQQQ	2010-02-11	2021-10-28
8	UDOW	2010-02-11	2021-10-28

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_1",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_3",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_5",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 40, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = None )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = None )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  8

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Multiple-LSTM  TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with Multiple-LSTM and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-29 18:23:46,474 [INFO] root: *** Adding long only trade strategies for : {'^IXIC', '^DJI'}

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "1999-07-01",
                             strBegDate         = "2000-01-01",
                             strEndDate         = "2005-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2008-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Slide Window WFA Backtest

tsExecuteEngine = BacktestExecutionSlideWindowWFA( \
            name                        = "SW WFA Backtest for subset of Portfolio D - non-anchored training",
            trade_asset_registered_item = portf_registered_obj,
            wfa_slide_win_size_list     = [ 3, 6] , # list of int, associated with wfa_slide_win_period_list                            
            wfa_slide_win_period_list   = [ WFASlideWindowPeriod.Months, WFASlideWindowPeriod.Months ],
            wfa_sw_scenario_obj         = WFASlideWinScenarioFixDatasets(fit_dataset = fit_datasets,
                                                                         test_period = testing_period ),
            anchor_fit_dataset          = False )  
        

tsExecuteEngine.evaluate()

Backtest Metrics

Execution & Investment Metrics

tsExecuteEngine.getSummaryMetrics().info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22 entries, 0 to 21
Data columns (total 72 columns):
 #   Column                      Non-Null Count  Dtype 
---  ------                      --------------  ----- 
 0   10Y (ann.)                  22 non-null     object
 1   1Y                          22 non-null     object
 2   3M                          22 non-null     object
 3   3Y (ann.)                   22 non-null     object
 4   5Y (ann.)                   22 non-null     object
 5   6M                          22 non-null     object
 6   All-time (ann.)             22 non-null     object
 7   Avg. Down Month             22 non-null     object
 8   Avg. Drawdown               22 non-null     object
 9   Avg. Drawdown Days          22 non-null     object
 10  Avg. Up Month               22 non-null     object
 11  Best Day                    22 non-null     object
 12  Best Month                  22 non-null     object
 13  Best Year                   22 non-null     object
 14  CAGR%                       22 non-null     object
 15  CPC Index                   22 non-null     object
 16  Calmar                      22 non-null     object
 17  Common Sense Ratio          22 non-null     object
 18  Cumulative Return           22 non-null     object
 19  Daily Value-at-Risk         22 non-null     object
 20  End Period                  22 non-null     object
 21  Expected Daily %            22 non-null     object
 22  Expected Monthly %          22 non-null     object
 23  Expected Shortfall (cVaR)   22 non-null     object
 24  Expected Yearly %           22 non-null     object
 25  Gain/Pain (1M)              22 non-null     object
 26  Gain/Pain Ratio             22 non-null     object
 27  Kelly Criterion             22 non-null     object
 28  Kurtosis                    22 non-null     object
 29  Longest DD Days             22 non-null     object
 30  MTD                         22 non-null     object
 31  Max Drawdown                22 non-null     object
 32  Outlier Loss Ratio          22 non-null     object
 33  Outlier Win Ratio           22 non-null     object
 34  Payoff Ratio                22 non-null     object
 35  Profit Factor               22 non-null     object
 36  Recovery Factor             22 non-null     object
 37  Risk of Ruin                22 non-null     object
 38  Risk-Free Rate              22 non-null     object
 39  Sharpe                      22 non-null     object
 40  Skew                        22 non-null     object
 41  Sortino                     22 non-null     object
 42  Sortino/√2                  22 non-null     object
 43  Start Period                22 non-null     object
 44  Tail Ratio                  22 non-null     object
 45  Time in Market              22 non-null     object
 46  Ulcer Index                 22 non-null     object
 47  Volatility (ann.)           22 non-null     object
 48  Win Days %                  22 non-null     object
 49  Win Month %                 22 non-null     object
 50  Win Quarter %               22 non-null     object
 51  Win Year %                  22 non-null     object
 52  Worst Day                   22 non-null     object
 53  Worst Month                 22 non-null     object
 54  Worst Year                  22 non-null     object
 55  YTD                         22 non-null     object
 56  anchor_fit_dataset          22 non-null     bool  
 57  index                       22 non-null     object
 58  is_portfolio                22 non-null     object
 59  model_type                  22 non-null     object
 60  ta_reg_item_id              0 non-null      object
 61  ta_reg_item_name            22 non-null     object
 62  test_period_len_days        22 non-null     int64 
 63  tr_period_len_days          22 non-null     int64 
 64  trade_asset                 22 non-null     object
 65  val_period_len_days         22 non-null     int64 
 66  wfa_slide_win_period        22 non-null     object
 67  wfa_slide_win_size          22 non-null     int64 
 68  wfa_sw_iter_no              22 non-null     int64 
 69  wfa_sw_name                 22 non-null     object
 70  wfa_sw_obj_size             22 non-null     int64 
 71  wfa_sw_scenario             22 non-null     object
dtypes: bool(1), int64(6), object(65)
memory usage: 12.3+ KB

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ [ 'wfa_sw_iter_no', 'wfa_slide_win_size', 'wfa_slide_win_period', 'tr_period_len_days', 'val_period_len_days',
    'test_period_len_days', 'ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 
    'Start Period', 'End Period', 'Time in Market ',
    'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	wfa_sw_iter_no	wfa_slide_win_size	wfa_slide_win_period	tr_period_len_days	val_period_len_days	test_period_len_days	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	1	3	Months	2191	729	365	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2008-01-03	2008-12-31	1.0	0.86	0.86	2.06	-0.19	0.11	-0.06
1	1	3	Months	2191	729	365	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2008-01-03	2008-12-31	1.0	0.81	0.81	1.74	-0.22	0.11	-0.07
2	1	3	Months	2191	729	365	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2008-01-03	2008-12-31	1.0	1.24	1.25	2.31	-0.19	0.11	-0.07
3	1	3	Months	2191	729	365	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2008-01-03	2008-12-31	1.0	1.56	1.57	2.67	-0.19	0.11	-0.07
4	1	3	Months	2191	729	365	all_^DJI	No	all	^DJI	2008-01-03	2008-12-31	1.0	1.14	1.15	2.19	-0.19	0.11	-0.07
5	1	3	Months	2191	729	365	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2008-01-03	2008-12-31	1.0	0.72	0.72	1.52	-0.2	0.12	-0.09
6	1	3	Months	2191	729	365	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2008-01-03	2008-12-31	1.0	0.83	0.84	1.65	-0.21	0.12	-0.08
7	1	3	Months	2191	729	365	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2008-01-03	2008-12-31	1.0	1.48	1.49	2.42	-0.2	0.12	-0.08
8	1	3	Months	2191	729	365	all_^IXIC	No	all	^IXIC	2008-01-03	2008-12-31	1.0	1.5	1.51	2.44	-0.2	0.12	-0.08
9	1	3	Months	2191	729	365	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2008-12-31	1.0	-0.4	-0.4	-1.02	-0.49	0.12	-0.09
10	1	3	Months	2191	729	365	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2008-12-31	1.0	-0.33	-0.33	-0.86	-0.42	0.11	-0.08
11	2	6	Months	2191	729	365	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2008-01-03	2008-12-31	1.0	0.88	0.88	2.06	-0.21	0.11	-0.06
12	2	6	Months	2191	729	365	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2008-01-03	2008-12-31	1.0	1.31	1.32	2.42	-0.22	0.11	-0.08
13	2	6	Months	2191	729	365	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2008-01-03	2008-12-31	1.0	1.3	1.31	2.38	-0.19	0.11	-0.07
14	2	6	Months	2191	729	365	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2008-01-03	2008-12-31	1.0	1.51	1.53	2.64	-0.22	0.11	-0.08
15	2	6	Months	2191	729	365	all_^DJI	No	all	^DJI	2008-01-03	2008-12-31	1.0	1.2	1.21	2.27	-0.22	0.11	-0.08
16	2	6	Months	2191	729	365	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2008-01-03	2008-12-31	1.0	1.18	1.19	2.1	-0.2	0.12	-0.08
17	2	6	Months	2191	729	365	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2008-01-03	2008-12-31	1.0	0.77	0.78	1.57	-0.23	0.12	-0.08
18	2	6	Months	2191	729	365	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2008-01-03	2008-12-31	1.0	1.03	1.04	1.91	-0.2	0.12	-0.08
19	2	6	Months	2191	729	365	all_^IXIC	No	all	^IXIC	2008-01-03	2008-12-31	1.0	1.18	1.19	2.1	-0.2	0.12	-0.08
20	2	6	Months	2191	729	365	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2008-12-31	1.0	-0.4	-0.4	-1.02	-0.49	0.12	-0.09
21	2	6	Months	2191	729	365	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2008-12-31	1.0	-0.33	-0.33	-0.86	-0.42	0.11	-0.08

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

SW-WFA-Backtest 2

Description:

• Portfolio has a subset of the trade strategies in Portfolio D
  • Smaller subset of AI models to reduce execution duration
• Re-fit AI trading models every 6 months within a backtest
• Slide Datasets by one year increments for five consecutive years
• Training-set is not anchored in outer slide window or during a backtest

Backtest:

1. Type: Slide Window WFA Backtest

2. Start AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2000-01-01 
        2. End Date   = 2005-12-31  
    2. Validation Period: 
        1. Begin Date = 2006-01-01 
        2. End Date   = 2007-12-31 

3. Final AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2004-01-01 
        2. End Date   = 2009-12-31  
    2. Validation Period: 
        1. Begin Date = 2010-01-01 
        2. End Date   = 2011-12-31   

4. Start Test Period: 
    1. Begin Date = 2008-01-01 
    2. End Date   = 2008-12-31

5. Final Test Period: 
    1. Begin Date = 2012-01-01 
    2. End Date   = 2012-12-31     

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "1980-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_From_1980",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-29 23:07:12,449 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_1",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #3

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_3",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

# TS #5

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm_5",     
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 40, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },
                             },
                    }  )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = None )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = None )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  8

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - Multiple-LSTM  TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with Multiple-LSTM and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-29 23:07:30,659 [INFO] root: *** Adding long only trade strategies for : {'^IXIC', '^DJI'}

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "1999-07-01",
                             strBegDate         = "2000-01-01",
                             strEndDate         = "2005-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2008-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Slide Window WFA Backtest

tsExecuteEngine = BacktestExecutionSlideWindowWFA( \
    name                        = "SW WFA Backtest-2 for subset of Portfolio D - non-anchored training",
    trade_asset_registered_item = portf_registered_obj,
    wfa_slide_win_size_list     = [ 6 ] , # list of int, associated with wfa_slide_win_period_list                   
    wfa_slide_win_period_list   = [ WFASlideWindowPeriod.Months ],
    wfa_sw_scenario_obj         = WFASlideWinScenarioSlidingDatasets(\
                                        fit_dataset               = fit_datasets,
                                        test_period               = testing_period,
                                        slide_win_size            = 1,
                                        slide_win_period          = WFASlideWindowPeriod.Years,
                                        end_date_last_test_period = datetime(year=2012, month=12, day=31) ),
    anchor_fit_dataset          = False )

tsExecuteEngine.evaluate()

Backtest Metrics

Performance Metrics

tsExecuteEngine.getSummaryMetrics()\
[ [ 'wfa_sw_iter_no', 'wfa_slide_win_size', 'wfa_slide_win_period', 'tr_period_len_days', 'val_period_len_days',
    'test_period_len_days', 'ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 
    'Start Period', 'End Period', 'Time in Market ',
    'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ]

	wfa_sw_iter_no	wfa_slide_win_size	wfa_slide_win_period	tr_period_len_days	val_period_len_days	test_period_len_days	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
0	1	6	Months	2191	729	365	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2008-01-03	2008-12-31	1.0	0.78	0.78	1.95	-0.18	0.11	-0.06
1	1	6	Months	2191	729	365	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2008-01-03	2008-12-31	1.0	0.94	0.94	1.93	-0.19	0.11	-0.07
2	1	6	Months	2191	729	365	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2008-01-03	2008-12-31	1.0	0.77	0.78	1.69	-0.24	0.11	-0.07
3	1	6	Months	2191	729	365	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2008-01-03	2008-12-31	1.0	1.56	1.57	2.68	-0.19	0.11	-0.07
4	1	6	Months	2191	729	365	all_^DJI	No	all	^DJI	2008-01-03	2008-12-31	1.0	0.82	0.82	1.76	-0.19	0.11	-0.07
5	1	6	Months	2191	729	365	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2008-01-03	2008-12-31	1.0	0.82	0.83	1.64	-0.19	0.12	-0.07
6	1	6	Months	2191	729	365	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2008-01-03	2008-12-31	1.0	1.32	1.33	2.25	-0.21	0.12	-0.07
7	1	6	Months	2191	729	365	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2008-01-03	2008-12-31	1.0	1.16	1.17	2.06	-0.23	0.12	-0.07
8	1	6	Months	2191	729	365	all_^IXIC	No	all	^IXIC	2008-01-03	2008-12-31	1.0	1.12	1.13	2.01	-0.23	0.12	-0.07
9	1	6	Months	2191	729	365	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2008-12-31	1.0	-0.4	-0.4	-1.02	-0.49	0.12	-0.09
10	1	6	Months	2191	729	365	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2008-12-31	1.0	-0.33	-0.33	-0.86	-0.42	0.11	-0.08
11	2	6	Months	2190	730	364	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2009-01-05	2009-12-31	1.0	0.2	0.2	1.05	-0.13	0.07	-0.04
12	2	6	Months	2190	730	364	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2009-01-05	2009-12-31	1.0	0.13	0.13	0.63	-0.18	0.07	-0.04
13	2	6	Months	2190	730	364	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2009-01-05	2009-12-31	1.0	0.09	0.09	0.48	-0.2	0.07	-0.04
14	2	6	Months	2190	730	364	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2009-01-05	2009-12-31	1.0	0.29	0.3	1.18	-0.14	0.07	-0.04
15	2	6	Months	2190	730	364	all_^DJI	No	all	^DJI	2009-01-05	2009-12-31	1.0	0.05	0.05	0.31	-0.21	0.07	-0.04
16	2	6	Months	2190	730	364	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2009-01-05	2009-12-31	1.0	0.16	0.16	0.66	-0.25	0.07	-0.04
17	2	6	Months	2190	730	364	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2009-01-05	2009-12-31	1.0	0.16	0.16	0.65	-0.27	0.07	-0.04
18	2	6	Months	2190	730	364	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2009-01-05	2009-12-31	1.0	0.26	0.26	0.95	-0.24	0.07	-0.04
19	2	6	Months	2190	730	364	all_^IXIC	No	all	^IXIC	2009-01-05	2009-12-31	1.0	0.3	0.3	1.06	-0.25	0.07	-0.04
20	2	6	Months	2190	730	364	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2009-01-05	2009-12-31	1.0	0.39	0.4	1.32	-0.23	0.07	-0.06
21	2	6	Months	2190	730	364	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2009-01-05	2009-12-31	1.0	0.15	0.16	0.72	-0.27	0.07	-0.05
22	3	6	Months	2190	730	364	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2010-01-05	2010-12-31	1.0	0.13	0.13	1.03	-0.06	0.04	-0.03
23	3	6	Months	2190	730	364	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2010-01-05	2010-12-31	1.0	0.22	0.22	1.32	-0.07	0.04	-0.04
24	3	6	Months	2190	730	364	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2010-01-05	2010-12-31	1.0	0.05	0.05	0.4	-0.12	0.04	-0.04
25	3	6	Months	2190	730	364	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2010-01-05	2010-12-31	1.0	0.13	0.13	0.85	-0.12	0.04	-0.04
26	3	6	Months	2190	730	364	all_^DJI	No	all	^DJI	2010-01-05	2010-12-31	1.0	0.08	0.08	0.56	-0.1	0.04	-0.04
27	3	6	Months	2190	730	364	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2010-01-05	2010-12-31	1.0	0.23	0.23	1.12	-0.18	0.05	-0.04
28	3	6	Months	2190	730	364	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2010-01-05	2010-12-31	1.0	0.06	0.06	0.38	-0.13	0.05	-0.04
29	3	6	Months	2190	730	364	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2010-01-05	2010-12-31	1.0	0.23	0.24	1.17	-0.1	0.05	-0.04
30	3	6	Months	2190	730	364	all_^IXIC	No	all	^IXIC	2010-01-05	2010-12-31	1.0	0.08	0.08	0.48	-0.13	0.05	-0.04
31	3	6	Months	2190	730	364	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2010-01-05	2010-12-31	1.0	0.15	0.15	0.81	-0.17	0.05	-0.04
32	3	6	Months	2190	730	364	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2010-01-05	2010-12-31	1.0	0.09	0.09	0.64	-0.13	0.04	-0.04
33	4	6	Months	2191	729	364	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2011-01-04	2011-12-30	1.0	-0.11	-0.11	-0.66	-0.19	0.05	-0.04
34	4	6	Months	2191	729	364	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2011-01-04	2011-12-30	1.0	-0.2	-0.21	-0.98	-0.25	0.05	-0.06
35	4	6	Months	2191	729	364	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2011-01-04	2011-12-30	1.0	-0.04	-0.04	-0.09	-0.17	0.06	-0.04
36	4	6	Months	2191	729	364	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2011-01-04	2011-12-30	1.0	-0.05	-0.05	-0.14	-0.16	0.06	-0.04
37	4	6	Months	2191	729	364	all_^DJI	No	all	^DJI	2011-01-04	2011-12-30	1.0	-0.11	-0.12	-0.47	-0.17	0.06	-0.04
38	4	6	Months	2191	729	364	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2011-01-04	2011-12-30	1.0	-0.17	-0.17	-0.62	-0.34	0.05	-0.07
39	4	6	Months	2191	729	364	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2011-01-04	2011-12-30	1.0	-0.17	-0.17	-0.59	-0.32	0.05	-0.07
40	4	6	Months	2191	729	364	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2011-01-04	2011-12-30	1.0	-0.2	-0.2	-0.75	-0.29	0.05	-0.07
41	4	6	Months	2191	729	364	all_^IXIC	No	all	^IXIC	2011-01-04	2011-12-30	1.0	-0.19	-0.2	-0.73	-0.35	0.05	-0.07
42	4	6	Months	2191	729	364	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2011-01-04	2011-12-30	1.0	-0.03	-0.03	-0.0	-0.19	0.05	-0.07
43	4	6	Months	2191	729	364	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2011-01-04	2011-12-30	1.0	0.05	0.05	0.32	-0.17	0.04	-0.06
44	5	6	Months	2191	729	365	Portfolio of US-Indexes with Multiple-LSTM and...	Yes			2012-01-04	2012-12-31	1.0	-0.01	-0.01	-0.04	-0.08	0.03	-0.03
45	5	6	Months	2191	729	365	ts_lstm_1_^DJI	No	ts_lstm_1	^DJI	2012-01-04	2012-12-31	1.0	-0.09	-0.09	-0.75	-0.11	0.02	-0.02
46	5	6	Months	2191	729	365	ts_lstm_3_^DJI	No	ts_lstm_3	^DJI	2012-01-04	2012-12-31	1.0	0.03	0.03	0.34	-0.1	0.02	-0.02
47	5	6	Months	2191	729	365	ts_lstm_5_^DJI	No	ts_lstm_5	^DJI	2012-01-04	2012-12-31	1.0	0.03	0.03	0.31	-0.12	0.02	-0.02
48	5	6	Months	2191	729	365	all_^DJI	No	all	^DJI	2012-01-04	2012-12-31	1.0	-0.03	-0.03	-0.17	-0.12	0.02	-0.02
49	5	6	Months	2191	729	365	ts_lstm_1_^IXIC	No	ts_lstm_1	^IXIC	2012-01-04	2012-12-31	1.0	-0.05	-0.05	-0.28	-0.1	0.03	-0.03
50	5	6	Months	2191	729	365	ts_lstm_3_^IXIC	No	ts_lstm_3	^IXIC	2012-01-04	2012-12-31	1.0	-0.05	-0.05	-0.25	-0.15	0.03	-0.03
51	5	6	Months	2191	729	365	ts_lstm_5_^IXIC	No	ts_lstm_5	^IXIC	2012-01-04	2012-12-31	1.0	-0.05	-0.05	-0.29	-0.1	0.03	-0.03
52	5	6	Months	2191	729	365	all_^IXIC	No	all	^IXIC	2012-01-04	2012-12-31	1.0	-0.07	-0.07	-0.44	-0.12	0.03	-0.03
53	5	6	Months	2191	729	365	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2012-01-04	2012-12-31	1.0	0.14	0.14	0.96	-0.12	0.03	-0.03
54	5	6	Months	2191	729	365	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2012-01-04	2012-12-31	1.0	0.06	0.06	0.54	-0.09	0.02	-0.02

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

SW-WFA-Backtest 3

Description:

• Portfolio has a subset of the trade strategies in Portfolio G
  • Smaller subset of AI models to reduce execution duration
• Re-fit AI trading models every 6 months within a backtest and anchored within the backtest
• Fit datasets are anchored and duration increased by one year increments for five consecutive years
  • Every sliding window iteration, the training set duration is increased by 9 months and the validation set by 3 months, the test set is just shifted by one year.
• Training-set is anchored in outer slide window and also anchored in the re-fitting process during a backtest

Backtest:

• Type: Slide Window WFA Backtest

• Iteration 1:

     train:       1/1/2000 to 12/31/2005
     validation:  1/1/2006 to 12/31/2007
     Test Period: 1/1/2008 to 12/31/2008

• Iteration 2:

     train:        1/1/2000  to  9/30/2006    (date range increased by nine-months)
     validation:  10/1/2006  to 12/31/2008    (date range increased by three-months and shifted 
                                               by nine-months)
     Test Period:  1/1/2009  to 12/31/2009    (date range shifted by one-year)

• Iteration 3:

     train:        1/1/2000  to  6/30/2007    (date range increased by nine-months)
     validation:   7/1/2007  to 12/31/2009    (date range increaed by three-months and shifted
                                               by nine-months)
     Test Period:  1/1/2010  to 12/31/2010    (date range shifted by one-year)
  
     ...
     ...

• Iteration Last:

     train:       1/1/2000 to xxx
     validation:  yyy      to 12/31/2011       
     Test Period: 1/1/2012 to 12/31/2012   

• Each iteration corresponds to a WFA Backtest, and in each WFA Backtest, AI Models are re-fitted every six months with the training and validation set for the WFA backtest anchored.

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "1980-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_From_1980",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-30 09:58:20,665 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

ts_model_pipline_mng_g = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_G",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_1", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )
# TS #3
    
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'   : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #8
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_2", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'          : 40,   'no_neurons_lay1'    : 20,
                                         'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                         'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                         'epochs'          : 20 }  
                                }
                            } }  )

# TS #10

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )

# TS #16

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_kama",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "kama",
                              'type'     : "ta",
                              'obj_args' : { 'window_fast' : 10, 'pow1_fast' : 2, 'pow2_fast'  : 30,
                                             'window_slow' : 10, 'pow1_slow' : 5, 'pow2_slow'  : 30 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #17

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_tsi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "tsi",
                              'type'     : "ta",
                              'obj_args' : { 'window_slow' : 25, 'window_fast' : 5 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_g )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_g )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  24

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - TechAnal & AI Trading Models TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with TechAnal & AI Trading Models, and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-30 09:59:37,285 [INFO] root: *** Adding long only trade strategies for : {'^IXIC', '^DJI', '^GSPC'}

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "1999-07-01",
                             strBegDate         = "2000-01-01",
                             strEndDate         = "2005-12-31" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2005-07-01",
                               strBegDate         = "2006-01-01",
                               strEndDate         = "2007-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2007-07-01",
                               strBegDate         = "2008-01-01",
                               strEndDate         = "2008-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute Slide Window WFA Backtest

tsExecuteEngine = BacktestExecutionSlideWindowWFA( \
    name                        = "SW WFA Backtest-3 for subset of Portfolio G - anchored training",
    trade_asset_registered_item = portf_registered_obj,
    wfa_slide_win_size_list     = [ 6 ] , # list of int, associated with wfa_slide_win_period_list                   
    wfa_slide_win_period_list   = [ WFASlideWindowPeriod.Months ],
    wfa_sw_scenario_obj         = WFASlideWinScenarioAnchoredSlidingDatasets(\
                                        fit_dataset               = fit_datasets,
                                        test_period               = testing_period,
                                        train_slide_win_size      = 9, # increase train set 9 months every sw incr
                                        val_slide_win_size        = 3, # increase val set 3 months every sw incr
                                                                       # and shift by 9 months
                                        slide_win_period          = WFASlideWindowPeriod.Months,
                                        end_date_last_test_period = datetime(year=2012, month=12, day=31) ),
    anchor_fit_dataset          = True )

tsExecuteEngine.evaluate()

Backtest Metrics

DJI

tsExecuteEngine.getSummaryMetrics()\
[ [ 'wfa_sw_iter_no', 'wfa_slide_win_size', 'wfa_slide_win_period', 'tr_period_len_days', 'val_period_len_days',
    'test_period_len_days', 'ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 
    'Start Period', 'End Period', 'Time in Market ',
    'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
query('trade_asset == "^DJI" ')

	wfa_sw_iter_no	wfa_slide_win_size	wfa_slide_win_period	tr_period_len_days	val_period_len_days	test_period_len_days	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
1	1	6	Months	2191	729	365	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2008-01-03	2008-12-31	1.0	0.88	0.88	1.84	-0.19	0.11	-0.07
2	1	6	Months	2191	729	365	ts_lstm_^DJI	No	ts_lstm	^DJI	2008-01-03	2008-12-31	1.0	0.83	0.83	1.78	-0.24	0.11	-0.07
3	1	6	Months	2191	729	365	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2008-01-03	2008-12-31	1.0	0.93	0.93	1.91	-0.24	0.11	-0.07
4	1	6	Months	2191	729	365	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2008-01-03	2008-12-31	1.0	0.96	0.96	1.95	-0.19	0.11	-0.07
5	1	6	Months	2191	729	365	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2008-01-03	2008-12-31	1.0	0.28	0.29	1.11	-0.09	0.06	-0.06
6	1	6	Months	2191	729	365	ts_kama_^DJI	No	ts_kama	^DJI	2008-01-03	2008-12-31	1.0	0.33	0.33	1.32	-0.09	0.05	-0.05
7	1	6	Months	2191	729	365	ts_tsi_^DJI	No	ts_tsi	^DJI	2008-01-03	2008-12-31	0.95	0.01	0.01	0.18	-0.2	0.07	-0.07
8	1	6	Months	2191	729	365	all_^DJI	No	all	^DJI	2008-01-03	2008-12-31	1.0	1.23	1.24	2.3	-0.19	0.11	-0.07
26	1	6	Months	2191	729	365	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2008-01-03	2008-12-31	1.0	-0.33	-0.33	-0.86	-0.42	0.11	-0.08
29	2	6	Months	2464	821	364	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2009-01-05	2009-12-31	1.0	0.19	0.19	0.84	-0.16	0.07	-0.04
30	2	6	Months	2464	821	364	ts_lstm_^DJI	No	ts_lstm	^DJI	2009-01-05	2009-12-31	1.0	0.16	0.16	0.74	-0.18	0.07	-0.04
31	2	6	Months	2464	821	364	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2009-01-05	2009-12-31	1.0	0.31	0.31	1.23	-0.13	0.07	-0.04
32	2	6	Months	2464	821	364	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2009-01-05	2009-12-31	1.0	0.18	0.18	0.79	-0.16	0.07	-0.04
33	2	6	Months	2464	821	364	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2009-01-05	2009-12-31	0.99	0.23	0.23	1.11	-0.17	0.04	-0.05
34	2	6	Months	2464	821	364	ts_kama_^DJI	No	ts_kama	^DJI	2009-01-05	2009-12-31	1.0	0.33	0.33	1.52	-0.19	0.04	-0.05
35	2	6	Months	2464	821	364	ts_tsi_^DJI	No	ts_tsi	^DJI	2009-01-05	2009-12-31	0.98	0.28	0.28	1.21	-0.11	0.07	-0.04
36	2	6	Months	2464	821	364	all_^DJI	No	all	^DJI	2009-01-05	2009-12-31	1.0	0.25	0.25	1.04	-0.11	0.07	-0.04
54	2	6	Months	2464	821	364	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2009-01-05	2009-12-31	1.0	0.15	0.16	0.72	-0.27	0.07	-0.05
57	3	6	Months	2737	913	364	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2010-01-05	2010-12-31	1.0	-0.01	-0.01	0.01	-0.13	0.04	-0.04
58	3	6	Months	2737	913	364	ts_lstm_^DJI	No	ts_lstm	^DJI	2010-01-05	2010-12-31	1.0	0.15	0.16	0.96	-0.08	0.04	-0.04
59	3	6	Months	2737	913	364	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2010-01-05	2010-12-31	1.0	-0.14	-0.15	-0.86	-0.27	0.04	-0.04
60	3	6	Months	2737	913	364	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2010-01-05	2010-12-31	1.0	0.28	0.28	1.61	-0.1	0.04	-0.04
61	3	6	Months	2737	913	364	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2010-01-05	2010-12-31	0.98	-0.12	-0.12	-0.71	-0.2	0.04	-0.03
62	3	6	Months	2737	913	364	ts_kama_^DJI	No	ts_kama	^DJI	2010-01-05	2010-12-31	1.0	-0.16	-0.16	-1.0	-0.27	0.04	-0.04
63	3	6	Months	2737	913	364	ts_tsi_^DJI	No	ts_tsi	^DJI	2010-01-05	2010-12-31	0.96	-0.03	-0.03	-0.12	-0.12	0.04	-0.04
64	3	6	Months	2737	913	364	all_^DJI	No	all	^DJI	2010-01-05	2010-12-31	1.0	0.07	0.07	0.49	-0.15	0.04	-0.04
82	3	6	Months	2737	913	364	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2010-01-05	2010-12-31	1.0	0.09	0.09	0.64	-0.13	0.04	-0.04
85	4	6	Months	3011	1003	364	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2011-01-04	2011-12-30	1.0	-0.1	-0.1	-0.38	-0.21	0.06	-0.04
86	4	6	Months	3011	1003	364	ts_lstm_^DJI	No	ts_lstm	^DJI	2011-01-04	2011-12-30	1.0	0.07	0.07	0.42	-0.15	0.06	-0.04
87	4	6	Months	3011	1003	364	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2011-01-04	2011-12-30	1.0	-0.11	-0.11	-0.43	-0.22	0.05	-0.06
88	4	6	Months	3011	1003	364	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2011-01-04	2011-12-30	1.0	-0.03	-0.03	-0.04	-0.2	0.06	-0.04
89	4	6	Months	3011	1003	364	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2011-01-04	2011-12-30	0.99	-0.09	-0.09	-0.36	-0.24	0.06	-0.04
90	4	6	Months	3011	1003	364	ts_kama_^DJI	No	ts_kama	^DJI	2011-01-04	2011-12-30	1.0	0.06	0.06	0.39	-0.12	0.05	-0.04
91	4	6	Months	3011	1003	364	ts_tsi_^DJI	No	ts_tsi	^DJI	2011-01-04	2011-12-30	0.96	-0.06	-0.06	-0.21	-0.16	0.05	-0.04
92	4	6	Months	3011	1003	364	all_^DJI	No	all	^DJI	2011-01-04	2011-12-30	1.0	0.02	0.02	0.2	-0.17	0.06	-0.04
110	4	6	Months	3011	1003	364	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2011-01-04	2011-12-30	1.0	0.05	0.05	0.32	-0.17	0.04	-0.06
113	5	6	Months	3286	1093	365	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2012-01-04	2012-12-31	1.0	-0.06	-0.06	-0.49	-0.16	0.02	-0.02
114	5	6	Months	3286	1093	365	ts_lstm_^DJI	No	ts_lstm	^DJI	2012-01-04	2012-12-31	1.0	-0.12	-0.13	-1.09	-0.14	0.02	-0.02
115	5	6	Months	3286	1093	365	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2012-01-04	2012-12-31	1.0	-0.04	-0.04	-0.3	-0.15	0.02	-0.02
116	5	6	Months	3286	1093	365	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2012-01-04	2012-12-31	1.0	0.03	0.03	0.29	-0.08	0.02	-0.02
117	5	6	Months	3286	1093	365	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2012-01-04	2012-12-31	0.98	-0.0	-0.0	0.03	-0.11	0.02	-0.02
118	5	6	Months	3286	1093	365	ts_kama_^DJI	No	ts_kama	^DJI	2012-01-04	2012-12-31	1.0	-0.07	-0.07	-0.6	-0.15	0.02	-0.02
119	5	6	Months	3286	1093	365	ts_tsi_^DJI	No	ts_tsi	^DJI	2012-01-04	2012-12-31	0.93	-0.08	-0.08	-0.74	-0.15	0.02	-0.02
120	5	6	Months	3286	1093	365	all_^DJI	No	all	^DJI	2012-01-04	2012-12-31	1.0	-0.07	-0.07	-0.61	-0.1	0.02	-0.02
138	5	6	Months	3286	1093	365	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2012-01-04	2012-12-31	1.0	0.06	0.06	0.54	-0.09	0.02	-0.02

Nasdaq

tsExecuteEngine.getSummaryMetrics()\
[ [ 'wfa_sw_iter_no', 'wfa_slide_win_size', 'wfa_slide_win_period', 'tr_period_len_days', 'val_period_len_days',
    'test_period_len_days', 'ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 
    'Start Period', 'End Period', 'Time in Market ',
    'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
query('trade_asset == "^IXIC" ')

	wfa_sw_iter_no	wfa_slide_win_size	wfa_slide_win_period	tr_period_len_days	val_period_len_days	test_period_len_days	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
9	1	6	Months	2191	729	365	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2008-01-03	2008-12-31	1.0	0.65	0.65	1.4	-0.28	0.12	-0.08
10	1	6	Months	2191	729	365	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2008-01-03	2008-12-31	1.0	1.24	1.25	2.16	-0.21	0.12	-0.07
11	1	6	Months	2191	729	365	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2008-01-03	2008-12-31	1.0	1.13	1.14	2.04	-0.21	0.12	-0.07
12	1	6	Months	2191	729	365	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2008-01-03	2008-12-31	1.0	0.93	0.94	1.78	-0.37	0.11	-0.12
13	1	6	Months	2191	729	365	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2008-01-03	2008-12-31	0.99	0.27	0.28	0.98	-0.19	0.09	-0.07
14	1	6	Months	2191	729	365	ts_kama_^IXIC	No	ts_kama	^IXIC	2008-01-03	2008-12-31	1.0	0.28	0.29	1.02	-0.22	0.08	-0.07
15	1	6	Months	2191	729	365	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2008-01-03	2008-12-31	0.96	0.33	0.33	1.14	-0.15	0.08	-0.06
16	1	6	Months	2191	729	365	all_^IXIC	No	all	^IXIC	2008-01-03	2008-12-31	1.0	1.52	1.53	2.41	-0.26	0.11	-0.12
25	1	6	Months	2191	729	365	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2008-01-03	2008-12-31	1.0	-0.4	-0.4	-1.02	-0.49	0.12	-0.09
37	2	6	Months	2464	821	364	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2009-01-05	2009-12-31	1.0	0.18	0.18	0.71	-0.19	0.07	-0.07
38	2	6	Months	2464	821	364	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2009-01-05	2009-12-31	1.0	-0.0	-0.0	0.13	-0.25	0.07	-0.04
39	2	6	Months	2464	821	364	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2009-01-05	2009-12-31	1.0	0.04	0.05	0.3	-0.2	0.07	-0.04
40	2	6	Months	2464	821	364	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2009-01-05	2009-12-31	1.0	0.12	0.12	0.53	-0.17	0.07	-0.06
41	2	6	Months	2464	821	364	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2009-01-05	2009-12-31	0.99	0.27	0.27	0.99	-0.21	0.05	-0.07
42	2	6	Months	2464	821	364	ts_kama_^IXIC	No	ts_kama	^IXIC	2009-01-05	2009-12-31	1.0	0.31	0.32	1.2	-0.21	0.05	-0.05
43	2	6	Months	2464	821	364	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2009-01-05	2009-12-31	0.97	-0.05	-0.05	-0.06	-0.23	0.07	-0.05
44	2	6	Months	2464	821	364	all_^IXIC	No	all	^IXIC	2009-01-05	2009-12-31	1.0	0.19	0.19	0.74	-0.19	0.07	-0.07
53	2	6	Months	2464	821	364	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2009-01-05	2009-12-31	1.0	0.39	0.4	1.32	-0.23	0.07	-0.06
65	3	6	Months	2737	913	364	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2010-01-05	2010-12-31	1.0	-0.13	-0.13	-0.59	-0.25	0.05	-0.04
66	3	6	Months	2737	913	364	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2010-01-05	2010-12-31	1.0	-0.12	-0.12	-0.53	-0.24	0.05	-0.04
67	3	6	Months	2737	913	364	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2010-01-05	2010-12-31	1.0	-0.12	-0.12	-0.53	-0.23	0.05	-0.04
68	3	6	Months	2737	913	364	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2010-01-05	2010-12-31	1.0	-0.19	-0.2	-0.98	-0.28	0.05	-0.04
69	3	6	Months	2737	913	364	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2010-01-05	2010-12-31	0.98	-0.2	-0.2	-1.08	-0.31	0.05	-0.04
70	3	6	Months	2737	913	364	ts_kama_^IXIC	No	ts_kama	^IXIC	2010-01-05	2010-12-31	1.0	-0.06	-0.06	-0.2	-0.18	0.05	-0.04
71	3	6	Months	2737	913	364	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2010-01-05	2010-12-31	0.96	0.06	0.06	0.38	-0.19	0.04	-0.05
72	3	6	Months	2737	913	364	all_^IXIC	No	all	^IXIC	2010-01-05	2010-12-31	1.0	-0.08	-0.08	-0.33	-0.18	0.05	-0.04
81	3	6	Months	2737	913	364	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2010-01-05	2010-12-31	1.0	0.15	0.15	0.81	-0.17	0.05	-0.04
93	4	6	Months	3011	1003	364	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2011-01-04	2011-12-30	1.0	-0.03	-0.03	0.0	-0.27	0.05	-0.07
94	4	6	Months	3011	1003	364	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2011-01-04	2011-12-30	1.0	0.13	0.13	0.61	-0.19	0.05	-0.07
95	4	6	Months	3011	1003	364	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2011-01-04	2011-12-30	1.0	0.1	0.1	0.49	-0.23	0.05	-0.07
96	4	6	Months	3011	1003	364	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2011-01-04	2011-12-30	1.0	-0.03	-0.03	0.0	-0.24	0.05	-0.07
97	4	6	Months	3011	1003	364	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2011-01-04	2011-12-30	0.97	-0.24	-0.24	-0.99	-0.37	0.05	-0.07
98	4	6	Months	3011	1003	364	ts_kama_^IXIC	No	ts_kama	^IXIC	2011-01-04	2011-12-30	1.0	-0.22	-0.22	-0.86	-0.31	0.07	-0.05
99	4	6	Months	3011	1003	364	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2011-01-04	2011-12-30	0.94	-0.17	-0.17	-0.68	-0.26	0.06	-0.04
100	4	6	Months	3011	1003	364	all_^IXIC	No	all	^IXIC	2011-01-04	2011-12-30	1.0	-0.08	-0.08	-0.19	-0.26	0.05	-0.07
109	4	6	Months	3011	1003	364	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2011-01-04	2011-12-30	1.0	-0.03	-0.03	-0.0	-0.19	0.05	-0.07
121	5	6	Months	3286	1093	365	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2012-01-04	2012-12-31	1.0	-0.17	-0.17	-1.14	-0.2	0.03	-0.03
122	5	6	Months	3286	1093	365	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2012-01-04	2012-12-31	1.0	-0.08	-0.08	-0.5	-0.15	0.03	-0.03
123	5	6	Months	3286	1093	365	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2012-01-04	2012-12-31	1.0	-0.17	-0.17	-1.15	-0.18	0.03	-0.03
124	5	6	Months	3286	1093	365	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2012-01-04	2012-12-31	1.0	-0.1	-0.1	-0.61	-0.15	0.03	-0.03
125	5	6	Months	3286	1093	365	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2012-01-04	2012-12-31	0.98	0.09	0.09	0.7	-0.08	0.03	-0.03
126	5	6	Months	3286	1093	365	ts_kama_^IXIC	No	ts_kama	^IXIC	2012-01-04	2012-12-31	1.0	-0.02	-0.02	-0.08	-0.13	0.03	-0.03
127	5	6	Months	3286	1093	365	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2012-01-04	2012-12-31	0.95	0.07	0.07	0.54	-0.14	0.03	-0.02
128	5	6	Months	3286	1093	365	all_^IXIC	No	all	^IXIC	2012-01-04	2012-12-31	1.0	-0.07	-0.07	-0.42	-0.13	0.03	-0.03
137	5	6	Months	3286	1093	365	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2012-01-04	2012-12-31	1.0	0.14	0.14	0.96	-0.12	0.03	-0.03

S&P 500

tsExecuteEngine.getSummaryMetrics()\
[ [ 'wfa_sw_iter_no', 'wfa_slide_win_size', 'wfa_slide_win_period', 'tr_period_len_days', 'val_period_len_days',
    'test_period_len_days', 'ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 
    'Start Period', 'End Period', 'Time in Market ',
    'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
query('trade_asset == "^GSPC" ')

	wfa_sw_iter_no	wfa_slide_win_size	wfa_slide_win_period	tr_period_len_days	val_period_len_days	test_period_len_days	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
17	1	6	Months	2191	729	365	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2008-01-03	2008-12-31	1.0	0.37	0.37	0.97	-0.25	0.12	-0.09
18	1	6	Months	2191	729	365	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2008-01-03	2008-12-31	1.0	-0.18	-0.18	-0.26	-0.43	0.12	-0.09
19	1	6	Months	2191	729	365	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2008-01-03	2008-12-31	1.0	0.48	0.48	1.15	-0.27	0.12	-0.09
20	1	6	Months	2191	729	365	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2008-01-03	2008-12-31	1.0	0.62	0.63	1.38	-0.25	0.12	-0.09
21	1	6	Months	2191	729	365	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2008-01-03	2008-12-31	0.99	0.42	0.42	1.46	-0.08	0.07	-0.06
22	1	6	Months	2191	729	365	ts_kama_^GSPC	No	ts_kama	^GSPC	2008-01-03	2008-12-31	1.0	0.38	0.38	1.44	-0.09	0.06	-0.05
23	1	6	Months	2191	729	365	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2008-01-03	2008-12-31	0.91	0.02	0.02	0.23	-0.19	0.08	-0.07
24	1	6	Months	2191	729	365	all_^GSPC	No	all	^GSPC	2008-01-03	2008-12-31	1.0	1.16	1.17	2.07	-0.19	0.12	-0.08
27	1	6	Months	2191	729	365	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2008-01-03	2008-12-31	1.0	-0.38	-0.38	-0.94	-0.48	0.12	-0.09
45	2	6	Months	2464	821	364	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2009-01-05	2009-12-31	1.0	0.42	0.43	1.43	-0.14	0.07	-0.05
46	2	6	Months	2464	821	364	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2009-01-05	2009-12-31	1.0	0.26	0.27	1.0	-0.26	0.07	-0.05
47	2	6	Months	2464	821	364	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2009-01-05	2009-12-31	1.0	0.42	0.43	1.44	-0.19	0.07	-0.05
48	2	6	Months	2464	821	364	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2009-01-05	2009-12-31	1.0	0.86	0.87	2.44	-0.15	0.07	-0.05
49	2	6	Months	2464	821	364	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2009-01-05	2009-12-31	0.98	0.25	0.26	1.03	-0.19	0.05	-0.06
50	2	6	Months	2464	821	364	ts_kama_^GSPC	No	ts_kama	^GSPC	2009-01-05	2009-12-31	1.0	0.27	0.27	1.15	-0.22	0.04	-0.05
51	2	6	Months	2464	821	364	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2009-01-05	2009-12-31	0.97	0.08	0.08	0.42	-0.15	0.07	-0.04
52	2	6	Months	2464	821	364	all_^GSPC	No	all	^GSPC	2009-01-05	2009-12-31	1.0	0.46	0.47	1.54	-0.12	0.07	-0.05
55	2	6	Months	2464	821	364	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2009-01-05	2009-12-31	1.0	0.2	0.2	0.8	-0.28	0.07	-0.05
73	3	6	Months	2737	913	364	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2010-01-05	2010-12-31	1.0	0.09	0.09	0.55	-0.15	0.04	-0.04
74	3	6	Months	2737	913	364	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2010-01-05	2010-12-31	1.0	-0.06	-0.06	-0.25	-0.19	0.04	-0.04
75	3	6	Months	2737	913	364	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2010-01-05	2010-12-31	1.0	-0.11	-0.11	-0.54	-0.2	0.04	-0.04
76	3	6	Months	2737	913	364	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2010-01-05	2010-12-31	1.0	-0.04	-0.04	-0.15	-0.18	0.04	-0.04
77	3	6	Months	2737	913	364	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2010-01-05	2010-12-31	0.98	-0.15	-0.15	-0.83	-0.25	0.04	-0.03
78	3	6	Months	2737	913	364	ts_kama_^GSPC	No	ts_kama	^GSPC	2010-01-05	2010-12-31	1.0	0.02	0.02	0.2	-0.15	0.04	-0.04
79	3	6	Months	2737	913	364	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2010-01-05	2010-12-31	0.95	0.01	0.01	0.14	-0.15	0.04	-0.04
80	3	6	Months	2737	913	364	all_^GSPC	No	all	^GSPC	2010-01-05	2010-12-31	1.0	-0.06	-0.06	-0.25	-0.2	0.04	-0.04
83	3	6	Months	2737	913	364	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2010-01-05	2010-12-31	1.0	0.11	0.11	0.67	-0.16	0.04	-0.04
101	4	6	Months	3011	1003	364	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2011-01-04	2011-12-30	1.0	-0.09	-0.09	-0.28	-0.22	0.05	-0.07
102	4	6	Months	3011	1003	364	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2011-01-04	2011-12-30	1.0	0.03	0.03	0.24	-0.15	0.05	-0.07
103	4	6	Months	3011	1003	364	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2011-01-04	2011-12-30	1.0	0.11	0.11	0.55	-0.13	0.05	-0.07
104	4	6	Months	3011	1003	364	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2011-01-04	2011-12-30	1.0	-0.09	-0.09	-0.28	-0.26	0.05	-0.07
105	4	6	Months	3011	1003	364	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2011-01-04	2011-12-30	0.98	-0.11	-0.11	-0.43	-0.23	0.07	-0.04
106	4	6	Months	3011	1003	364	ts_kama_^GSPC	No	ts_kama	^GSPC	2011-01-04	2011-12-30	1.0	0.03	0.03	0.25	-0.13	0.06	-0.04
107	4	6	Months	3011	1003	364	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2011-01-04	2011-12-30	0.94	-0.15	-0.15	-0.67	-0.22	0.06	-0.04
108	4	6	Months	3011	1003	364	all_^GSPC	No	all	^GSPC	2011-01-04	2011-12-30	1.0	-0.05	-0.05	-0.11	-0.19	0.05	-0.07
111	4	6	Months	3011	1003	364	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2011-01-04	2011-12-30	1.0	-0.01	-0.01	0.07	-0.19	0.05	-0.07
129	5	6	Months	3286	1093	365	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2012-01-04	2012-12-31	1.0	-0.15	-0.16	-1.27	-0.19	0.02	-0.02
130	5	6	Months	3286	1093	365	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2012-01-04	2012-12-31	1.0	-0.06	-0.06	-0.43	-0.15	0.02	-0.02
131	5	6	Months	3286	1093	365	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2012-01-04	2012-12-31	1.0	0.19	0.19	1.46	-0.09	0.02	-0.02
132	5	6	Months	3286	1093	365	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2012-01-04	2012-12-31	1.0	-0.07	-0.08	-0.54	-0.12	0.02	-0.03
133	5	6	Months	3286	1093	365	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2012-01-04	2012-12-31	0.98	0.08	0.08	0.68	-0.08	0.02	-0.02
134	5	6	Months	3286	1093	365	ts_kama_^GSPC	No	ts_kama	^GSPC	2012-01-04	2012-12-31	0.99	-0.11	-0.11	-0.84	-0.21	0.02	-0.03
135	5	6	Months	3286	1093	365	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2012-01-04	2012-12-31	0.95	-0.04	-0.04	-0.28	-0.14	0.02	-0.02
136	5	6	Months	3286	1093	365	all_^GSPC	No	all	^GSPC	2012-01-04	2012-12-31	1.0	-0.06	-0.06	-0.45	-0.18	0.02	-0.03
139	5	6	Months	3286	1093	365	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2012-01-04	2012-12-31	1.0	0.12	0.12	0.94	-0.1	0.02	-0.02

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Reports

The reports currently supported are provided by quantstats, https://github.com/ranaroussi/quantstats.

• Guidelines:
  1. Run a backtest (Backtest, WFA Backtest )
  2. Select the Report to execute
  3. Select a trade strategy from the backtest for the report
  4. Optional, select a trade strategy from the backtest to use as a benchmark for the report

from trade_engine.reports.BacktestReports import BacktestReports

WFA-Backtest for Reports

Description:

• Portfolio has a subset of the trade strategies in Portfolio G
  • Smaller subset of TA and AI models to reduce execution duration
• This WFA-Backtest is similar to WFA-Backtest 3, except for:
  • the length of
  • the Traning and Validation periods are reduced by 1/3 duration
  • Re-fit AI trading models every 3 months
  • Training-set is not anchored

Backtest:

1. Type: WFA Backtest
2. AI Models Fit Date Ranges
    1. Training Period: 
        1. Begin Date = 2008-05-01 
        2. End Date   = 2010-04-30  
    2. Validation Period: 
        1. Begin Date = 2010-05-01 
        2. End Date   = 2010-12-31   
4. Test Period: 
    1. Begin Date = 2011-01-01 
    2. End Date   = 2012-12-31  

Portfolio Trade Assets

portfolio_trade_assets   = [ "^DJI", "^IXIC", "^GSPC" ]

Market Data Manager

dirPathMarketData = '/Users/gustavozambrana/Google Drive/Python/Projects/Trade_Engine/Data/US_Market_Indixes'

supportedTradeAssets   = [ "^DJI", "^IXIC", "^GSPC", "UDOW", "SDOW", "SPXL", "SPXS", "TQQQ", "SQQQ" ]
beg_date_str           = "1980-01-01"
end_date_str           = None

ts_marketdat_mng = TradeStrategyMarketDataMng(\
        marketDataProviderName = "yahoofinance",
        supportedTradeAssets   = supportedTradeAssets,
        stock_price_col_name   = "Adj Close",
        fileNameMarketData     = "US_Indixes_And_ETFs_MrkData_From_1980",
        dirPathMarketData      = dirPathMarketData,
        beg_date_str           = beg_date_str,
        end_date_str           = end_date_str,
        interval               = "1d"  ) 

2021-10-30 23:36:10,769 [INFO] root: Found previous Market data previously loaded to specified file and dir.

Pipeline Data Manager

ts_model_pipline_mng_g = TradeStrategyModelPipelineDataMng(\
                            tsMarketDataMng    = ts_marketdat_mng,
                            package            = "trade_engine.model.ai.WaveNet_RNN_AI_PipelineBuilder",
                            model_class_name   = "WaveNet_RNN_AI_PipelineBuilder",
                            fileNameMarketData = "WaveNet_RNN_AI_Pipeline_Data_Portf_G",
                            forceCreatePipelineData = True,
                            dirPathMarketData  = dirPathMarketData )

Simulation Broker

brokerTradingObj = SimBrokerTrading()

Trade Strategies, Trade Investment Manager, and Trade Asset Register Items

tsConfDat = TradeStrategyConfigData()

# Set Config

tsConfDat.tsSetConfig(\
    ts_set_conf_dict = 
           { 'set_actions_conf' : 
                      { "entry" : { 'votingPolicy' : 1, 'incNoInvestInVotingDec' : False,
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1, 'cross_over_signal_for_trading' : False },
                        "exit"  : { 'votingPolicy' : 1, 
                                    'minPerVotingLong' : 0.0, 'minPerVotingShort' : 0.0,
                                    'lastn_match' : 1 } 
                      }
           } )
           
# TS #1

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_1", 
    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps' : 50, 'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'         : 20 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 1, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 } },

                               "stop_lost" :
                                    { 'model'    : "fix_perc",
                                      'type'     : "ta",
                                      'obj_args' : { 'long_stop_los_per' : 5, 'short_stop_los_per'    : 5 }  } 
                             },
                    }  )

# TS #2

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_lstm", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "lstm",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'         : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn'   : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )
# TS #3
    
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_conv1d_gru", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "conv1d_gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'   : 50,   'no_neurons_lay1'    : 20,
                                                 'no_neurons_lay2' : 20,   'pred_ahead_periods' : 5,
                                                 'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                                 'epochs'          : 20 }  
                                }
                            } }  )

# TS #8
tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_gru_2", 
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "gru",
                                  'type'     : "ai",
                                  'obj_args' : { 'n_steps'          : 40,   'no_neurons_lay1'    : 20,
                                         'no_neurons_lay2' : 20,   'pred_ahead_periods' : 3,
                                         'ext_trade_dyn'   : True, 're_eval_exit_dyn' : True,
                                         'epochs'          : 20 }  
                                }
                            } }  )

# TS #10

tsConfDat.addTSConf(\
   ts_conf_dict = { 
          'ts_unique_tag' : "ts_sma_2",    
          'actions_conf'  : { "entry_exit" :  
                                { 'model'    : "sma",
                                  'type'     : "ta",
                                  'obj_args' : { 'win_fast' : 15, 'win_slow' : 60 } ,
                                  'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                                  'exit_policy_args'   : { 'lastn_match' : 1 },
                                },
                            }  } )

# TS #16

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_kama",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "kama",
                              'type'     : "ta",
                              'obj_args' : { 'window_fast' : 10, 'pow1_fast' : 2, 'pow2_fast'  : 30,
                                             'window_slow' : 10, 'pow1_slow' : 5, 'pow2_slow'  : 30 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )
# TS #17

tsConfDat.addTSConf(\
   ts_conf_dict = { 
      'ts_unique_tag' : "ts_tsi",    
      'actions_conf'  : { "entry_exit" :  
                            { 'model'    : "tsi",
                              'type'     : "ta",
                              'obj_args' : { 'window_slow' : 25, 'window_fast' : 5 },
                              'entry_policy_args'  : { 'lastn_match' : 2, 'cross_enter_signal' : False },
                              'exit_policy_args'   : { 'lastn_match' : 1 },
                            },
                        }  } )

taRegisterItems = [ ]
tsActionGen = TradeStrategyActionGenerator()

for trade_asset in portfolio_trade_assets :
    
    num_ts = len(tsConfDat.name_list) + 1  # adding the composite  trade strategy
    
    for i in range( num_ts ) :

        if i <= num_ts - 2:
            # *** Trade Strategy 

            actionDatItem  = tsConfDat.getTSConfigDataForItem(item_num = i )
            ts_unique_name = actionDatItem.name_list[0]
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = actionDatItem,
                                                     pipelineMng       = ts_model_pipline_mng_g )
        else :
            # Composite Trade Strategy 
            
            ts_unique_name = "all"            
            trade_enter_set, trade_exit_set = tsActionGen.createEnterAndExitActionObjs ( \
                                                     ts_marketdat_mng  = ts_marketdat_mng, 
                                                     test_trade_asset  = trade_asset, 
                                                     dirPathMarketData = dirPathMarketData,
                                                     actionData        = tsConfDat,
                                                     pipelineMng       = ts_model_pipline_mng_g )

        ts_model = TradeStrategyBaseModelPersist(\
                        trade_asset               = trade_asset,
                        ts_marketdat_mng          = ts_marketdat_mng,
                        ts_enter                  = trade_enter_set,
                        ts_exit                   = trade_exit_set,                       
                        model_type                = ts_unique_name,
                        model_version             = "1.0",
                        market_data_period        = MarketDataPeriod.Day,
                    ) 

        # *** Trade Strategy Investment Manager 

        taInvMng = TradingAssetInvMngBase(\
                                        trade_asset                = trade_asset,
                                        minPeriodsBetweenTrades    = 1,
                                        initInvestment             = 1000000.0, 
                                        capPercentToInvestPerTrade = 100.0,
                                        minInvestPercent           = 5.0,
                                        min_account_balance        = 1000.0 )

        # *** Trade Register Item
        # *** This object has all configuration defined to execute a given trade strategy,
        # *** It can be executed individually in backtest or as part of a portfolio

        ta_registered_item = TradingAssetRegisterItem(\
                trade_name                    = "{0}_{1}".format(ts_unique_name, trade_asset), 
                ts_model                      = ts_model,
                tradingExecutionType          = TradingAssetExecutionType.Simulation,
                tradingBrokerObj              = brokerTradingObj,
                tradingAssetInvMngObj         = taInvMng )  

        taRegisterItems.append(ta_registered_item)

print("Number of configured Trade Asset Register Items: ", len(taRegisterItems) )

Number of configured Trade Asset Register Items:  24

Portfolio Investment Manager

# *** Porttfolio Investment Manager across all individual trade strategies investment managers
# *** Role: 
# ***    1) Managed investment funds across n number of trade strategies
# ***    2) Rebalance portfolio periodically or never
# ***    3) Portfolio Policy Weights 
# ***          equal weights (default)
# ***          MinimumVariance
# ***          User defined 
# ***    4) Track Portfolio level returns and metrics
# ***          Individual Trade Strategies return and metrics tracked through their 
# ***          investment managers
# ***          Portfolio funds are split across each trade strategy investment manager based on the
# ***          portfolio weights

portfInvMng = PortfolioInvMng(\
                  name                       = "Portfolio Inv. Manager for US-Indexes - TechAnal & AI Trading Models TS",
                  initInvestment             = 2000000.0 * ( len(taRegisterItems) + 3),  # 200K/trade-strag
                  portfRebalanceFreq         = PortfolioRebalanceFrequency.Never,
                  portfRebalancePeriods      = 1,
                  minInvestPercent           = 5.0,
                  min_account_balance        = 1000.0,     
                  minPeriodsBetweenTrades    = 1,
                  capPercentToInvestPerTrade = 100.0 )

Trade Register Items Portfolio

# *** Set of Trade Register Items
#
# Configuration Summary:
#    - Each set has 1 to n trade register items as previously described 
#    - Optionally add long only trade strategies for all unique trade assets in the
#      portfolio 
#           - (Dow, Nasdaq, and S&P 500 for this portfolio) in this example
#           - Might be useful for bench marking trade strategies
#    - Each set has one Investment Portfolio Manager
#       - portfolio funds allocated per portfolio policy rules and re-balanced (if defined), 
#         across all trade strategies


portf_registered_obj = PortfolioRegisterItem(\
                        name                 = "Portfolio of US-Indexes with TechAnal & AI Trading Models, and Long strategies",
                        taRegisterItems      = taRegisterItems,
                        portfolioInvMngObj   = portfInvMng,
                        tradingExecutionType = TradingAssetExecutionType.Simulation,
                        add_long_only_ts     = True )

2021-10-30 23:36:38,106 [INFO] root: *** Adding long only trade strategies for : {'^IXIC', '^DJI', '^GSPC'}

Training, Validation and Test Periods for Backtest

In Order to backtest a trade strategy or portfolio that has AI models, the following data is required:

1. Training Date Range

   1. Date range that will be used to train the AI models in the backtest

2. Validation Date Range

   1. Date range that twill be used to validate the AI models, should not overlapped with the training date range

3. Test Period Date Range

   1. This is the out-sample date range, which corresponds to the date range of the backtest

Date Ranges are defined with a DatasetDef object, and have the following parameters:

1. strBegDateAIModels : Date to begin training or validation of an AI model
2. strBegDate : Date to begin the backtest
3. strEndDate : Date to end training, validation and Backtest

Note:

1. The param strBegDateAIModels can be defined as the same date as strBegDate, that is the default, if not defined.
2. The critical aspect of using this parameter is in the backtest. Some AI Models, such as time series, others using moving averages over many periods, create params with no predicting data for those initial dates. Thus, in order to ensure the backtest starts on the actual date specified, the strBegDateAIModels should be defined as an earlier date, and it should not overlapped with the validation period end-date to avoid any chance of bias by picking at the validation data.

training_period = DatasetDef(datasetType = DatasetType.Training,
                             strBegDateAIModels = "2007-11-01",
                             strBegDate         = "2008-05-01",
                             strEndDate         = "2010-04-30" )

validation_period = DatasetDef(datasetType = DatasetType.Validation,
                               strBegDateAIModels = "2009-11-01",
                               strBegDate         = "2010-05-01",
                               strEndDate         = "2010-12-31" )

testing_period = DatasetDef(datasetType = DatasetType.Testing,
                               strBegDateAIModels = "2010-07-01",
                               strBegDate         = "2011-01-01",
                               strEndDate         = "2012-12-31" )

fit_datasets = FitDatasetsDef( trainingDataset    = training_period,
                               validationDataset  = validation_period )

Execute WFA Backtest

tsExecuteEngine = BacktestExecutionWFA( \
            name                        = "WFA Backtest-4 for subset of Portfolio G - anchored training-set",
            test_period                 = testing_period,
            fit_datasets_start          = fit_datasets,
            trade_asset_registered_item = portf_registered_obj,
            wfa_slide_win_size          = 3,
            wfa_slide_win_period        = WFASlideWindowPeriod.Months, 
            anchor_fit_dataset          = False )                      

tsExecuteEngine.evaluate()

Backtest Metrics

Trade Strategies Execution & Investment Metrics

DOW Jones

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("DJI")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
1	ts_gru_1_^DJI	No	94	2000000.0	2.409690e+06	0.204845	Completed
2	ts_lstm_^DJI	No	67	2000000.0	2.531228e+06	0.265614	Completed
3	ts_conv1d_gru_^DJI	No	89	2000000.0	2.360190e+06	0.180095	Completed
4	ts_gru_2_^DJI	No	144	2000000.0	1.890071e+06	-0.054964	Completed
5	ts_sma_2_^DJI	No	11	2000000.0	1.841726e+06	-0.079137	Completed
6	ts_kama_^DJI	No	8	2000000.0	1.988360e+06	-0.005820	Completed
7	ts_tsi_^DJI	No	26	2000000.0	1.758361e+06	-0.120820	Completed
8	all_^DJI	No	92	2000000.0	2.366672e+06	0.183336	Completed
26	Long-Only Trade Strategy for ^DJI	No	1	2000000.0	2.245110e+06	0.122555	Completed

Nasdaq

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("IXIC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
9	ts_gru_1_^IXIC	No	124	2000000.0	2.126741e+06	0.063371	Completed
10	ts_lstm_^IXIC	No	80	2000000.0	3.885654e+06	0.942827	Completed
11	ts_conv1d_gru_^IXIC	No	91	2000000.0	3.330966e+06	0.665483	Completed
12	ts_gru_2_^IXIC	No	148	2000000.0	2.013696e+06	0.006848	Completed
13	ts_sma_2_^IXIC	No	16	2000000.0	1.640432e+06	-0.179784	Completed
14	ts_kama_^IXIC	No	11	2000000.0	1.496984e+06	-0.251508	Completed
15	ts_tsi_^IXIC	No	25	2000000.0	1.806895e+06	-0.096552	Completed
16	all_^IXIC	No	107	2000000.0	2.568200e+06	0.284100	Completed
25	Long-Only Trade Strategy for ^IXIC	No	1	2000000.0	2.243697e+06	0.121848	Completed

S&P 500

tsExecuteEngine.getExecutionMetrics()[ ['TA_Reg_Name', 'Is_Portfolio', 'Num_Trades_Completed',
        'Init_Capital', 'Final_Capital', 'Cum_Return', 'Backtest_Status']].\
        query('TA_Reg_Name.str.contains("GSPC")', engine='python')

	TA_Reg_Name	Is_Portfolio	Num_Trades_Completed	Init_Capital	Final_Capital	Cum_Return	Backtest_Status
17	ts_gru_1_^GSPC	No	110	2000000.0	2.180504e+06	0.090252	Completed
18	ts_lstm_^GSPC	No	57	2000000.0	2.503388e+06	0.251694	Completed
19	ts_conv1d_gru_^GSPC	No	102	2000000.0	2.625871e+06	0.312935	Completed
20	ts_gru_2_^GSPC	No	122	2000000.0	2.002088e+06	0.001044	Completed
21	ts_sma_2_^GSPC	No	12	2000000.0	1.942915e+06	-0.028543	Completed
22	ts_kama_^GSPC	No	8	2000000.0	1.809392e+06	-0.095304	Completed
23	ts_tsi_^GSPC	No	27	2000000.0	1.653393e+06	-0.173303	Completed
24	all_^GSPC	No	83	2000000.0	1.958139e+06	-0.020930	Completed
27	Long-Only Trade Strategy for ^GSPC	No	1	2000000.0	2.242591e+06	0.121295	Completed

Performance Metrics

Dow Jones

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^DJI" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
1	ts_gru_1_^DJI	No	ts_gru_1	^DJI	2011-01-04	2012-12-31	1.0	0.2	0.1	0.63	-0.14	0.05	-0.06
2	ts_lstm_^DJI	No	ts_lstm	^DJI	2011-01-04	2012-12-31	1.0	0.27	0.13	0.78	-0.18	0.04	-0.06
3	ts_conv1d_gru_^DJI	No	ts_conv1d_gru	^DJI	2011-01-04	2012-12-31	1.0	0.18	0.09	0.57	-0.17	0.04	-0.06
4	ts_gru_2_^DJI	No	ts_gru_2	^DJI	2011-01-04	2012-12-31	1.0	-0.05	-0.03	-0.08	-0.19	0.05	-0.06
5	ts_sma_2_^DJI	No	ts_sma_2	^DJI	2011-01-04	2012-12-31	0.99	-0.08	-0.04	-0.17	-0.24	0.06	-0.04
6	ts_kama_^DJI	No	ts_kama	^DJI	2011-01-04	2012-12-31	1.0	-0.01	-0.0	0.06	-0.16	0.05	-0.04
7	ts_tsi_^DJI	No	ts_tsi	^DJI	2011-01-04	2012-12-31	0.94	-0.12	-0.06	-0.34	-0.21	0.05	-0.04
8	all_^DJI	No	all	^DJI	2011-01-04	2012-12-31	1.0	0.18	0.09	0.58	-0.2	0.05	-0.06
26	Long-Only Trade Strategy for ^DJI	No	long	^DJI	2011-01-04	2012-12-31	1.0	0.12	0.06	0.43	-0.17	0.04	-0.06

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^DJI" ').T.head(60)

Nasdaq

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^IXIC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
9	ts_gru_1_^IXIC	No	ts_gru_1	^IXIC	2011-01-04	2012-12-31	1.0	0.06	0.03	0.25	-0.28	0.05	-0.07
10	ts_lstm_^IXIC	No	ts_lstm	^IXIC	2011-01-04	2012-12-31	1.0	0.94	0.4	1.72	-0.17	0.05	-0.07
11	ts_conv1d_gru_^IXIC	No	ts_conv1d_gru	^IXIC	2011-01-04	2012-12-31	1.0	0.67	0.29	1.34	-0.19	0.05	-0.07
12	ts_gru_2_^IXIC	No	ts_gru_2	^IXIC	2011-01-04	2012-12-31	1.0	0.01	0.0	0.12	-0.24	0.05	-0.07
13	ts_sma_2_^IXIC	No	ts_sma_2	^IXIC	2011-01-04	2012-12-31	0.97	-0.18	-0.09	-0.39	-0.37	0.05	-0.07
14	ts_kama_^IXIC	No	ts_kama	^IXIC	2011-01-04	2012-12-31	1.0	-0.25	-0.14	-0.61	-0.31	0.07	-0.05
15	ts_tsi_^IXIC	No	ts_tsi	^IXIC	2011-01-04	2012-12-31	0.95	-0.1	-0.05	-0.17	-0.26	0.06	-0.04
16	all_^IXIC	No	all	^IXIC	2011-01-04	2012-12-31	1.0	0.28	0.13	0.7	-0.22	0.05	-0.07
25	Long-Only Trade Strategy for ^IXIC	No	long	^IXIC	2011-01-04	2012-12-31	1.0	0.12	0.06	0.38	-0.19	0.05	-0.07

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^IXIC" ').T.head(60)

S&P 500

tsExecuteEngine.getSummaryMetrics()\
[ ['ta_reg_item_name', 'is_portfolio', 'model_type', 'trade_asset', 'Start Period', 'End Period', 'Time in Market ',
   'Cumulative Return ', 'CAGR%', 'Sharpe', 'Max Drawdown ', 'Best Day ', 'Worst Day '] ].\
        query('trade_asset == "^GSPC" ')

	ta_reg_item_name	is_portfolio	model_type	trade_asset	Start Period	End Period	Time in Market	Cumulative Return	CAGR%	Sharpe	Max Drawdown	Best Day	Worst Day
17	ts_gru_1_^GSPC	No	ts_gru_1	^GSPC	2011-01-04	2012-12-31	1.0	0.09	0.04	0.32	-0.2	0.05	-0.07
18	ts_lstm_^GSPC	No	ts_lstm	^GSPC	2011-01-04	2012-12-31	1.0	0.25	0.12	0.69	-0.17	0.05	-0.07
19	ts_conv1d_gru_^GSPC	No	ts_conv1d_gru	^GSPC	2011-01-04	2012-12-31	1.0	0.31	0.15	0.81	-0.18	0.05	-0.07
20	ts_gru_2_^GSPC	No	ts_gru_2	^GSPC	2011-01-04	2012-12-31	1.0	0.0	0.0	0.1	-0.22	0.05	-0.07
21	ts_sma_2_^GSPC	No	ts_sma_2	^GSPC	2011-01-04	2012-12-31	0.98	-0.03	-0.01	0.01	-0.23	0.07	-0.04
22	ts_kama_^GSPC	No	ts_kama	^GSPC	2011-01-04	2012-12-31	1.0	-0.1	-0.05	-0.2	-0.23	0.06	-0.04
23	ts_tsi_^GSPC	No	ts_tsi	^GSPC	2011-01-04	2012-12-31	0.95	-0.17	-0.09	-0.47	-0.24	0.06	-0.04
24	all_^GSPC	No	all	^GSPC	2011-01-04	2012-12-31	1.0	-0.02	-0.01	0.04	-0.22	0.05	-0.07
27	Long-Only Trade Strategy for ^GSPC	No	long	^GSPC	2011-01-04	2012-12-31	1.0	0.12	0.06	0.4	-0.19	0.05	-0.07

tsExecuteEngine.getSummaryMetrics().query('trade_asset == "^GSPC" ').T.head(60)

Persist Backtest to DB (optional )

session.add( tsExecuteEngine )

try :
    session.commit()
except Exception as inst:
    print(type(inst))    # the exception instance
    print(inst)
    print(inst.args)     # arguments stored in .args
    session.rollback()   # roll back changes
finally:
    print(" done")

Full Tearsheet Report for a Trade Strategy

Composite Nasdaq Trade Strategy vs Nasdaq

BacktestReports.createTearsheetReport(\
                    backtest                   = tsExecuteEngine,
                    ta_reg_item_name           = "all_^IXIC",
                    ta_reg_item_benchmark_name = "Long-Only Trade Strategy for ^IXIC ", # optional
                    report_type                = "full" )

Performance Metrics

                           Strategy    Benchmark
-------------------------  ----------  -----------
Start Period               2011-01-04  2011-01-04
End Period                 2012-12-31  2012-12-31
Risk-Free Rate             0.0%        0.0%
Time in Market             100.0%      100.0%

Cumulative Return          28.41%      12.18%
CAGR%                      13.38%      5.94%

Sharpe                     0.7         0.38
Sortino                    1.06        0.54
Sortino/√2                 0.75        0.38

Max Drawdown               -22.15%     -18.71%
Longest DD Days            266         277
Volatility (ann.)          20.96%      20.75%
R^2                        0.08        0.08
Calmar                     0.6         0.32
Skew                       0.11        -0.29
Kurtosis                   3.09        3.24

Expected Daily %           0.05%       0.02%
Expected Monthly %         1.05%       0.48%
Expected Yearly %          13.32%      5.92%
Kelly Criterion            2.6%        5.8%
Risk of Ruin               0.0%        0.0%
Daily Value-at-Risk        -2.11%      -2.12%
Expected Shortfall (cVaR)  -2.11%      -2.12%

Gain/Pain Ratio            0.13        0.07
Gain/Pain (1M)             1.07        0.51

Payoff Ratio               0.99        0.99
Profit Factor              1.13        1.07
Common Sense Ratio         1.33        1.09
CPC Index                  0.58        0.56
Tail Ratio                 1.18        1.02
Outlier Win Ratio          3.91        4.17
Outlier Loss Ratio         3.57        3.4

MTD                        4.51%       0.31%
3M                         -1.31%      -3.1%
6M                         0.71%       2.88%
YTD                        3.85%       15.9%
1Y                         3.85%       15.9%
3Y (ann.)                  13.38%      5.94%
5Y (ann.)                  13.38%      5.94%
10Y (ann.)                 13.38%      5.94%
All-time (ann.)            13.38%      5.94%

Best Day                   5.29%       5.29%
Worst Day                  -6.9%       -6.9%
Best Month                 9.74%       11.14%
Worst Month                -6.81%      -7.19%
Best Year                  23.65%      15.9%
Worst Year                 3.85%       -3.21%

Avg. Drawdown              -4.32%      -3.36%
Avg. Drawdown Days         34          37
Recovery Factor            1.28        0.65
Ulcer Index                1.08        1.08

Avg. Up Month              2.5%        4.14%
Avg. Down Month            -4.14%      -2.51%
Win Days %                 51.5%       53.09%
Win Month %                75.0%       54.17%
Win Quarter %              62.5%       50.0%
Win Year %                 100.0%      50.0%
Beta                       0.29        -
Alpha                      0.12        -

None

5 Worst Drawdowns

	Start	Valley	End	Days	Max Drawdown	99% Max Drawdown
1	2011-02-22	2011-08-08	2011-09-07	197	-22.151774	-16.385371
2	2011-09-14	2011-10-14	2011-11-04	51	-14.116201	-13.698558
3	2012-04-09	2012-06-08	2012-12-31	266	-12.779965	-11.894544
4	2011-11-16	2011-11-25	2011-12-09	23	-9.108071	-8.416839
5	2012-03-13	2012-03-22	2012-04-04	22	-3.438963	-3.294040

Strategy Visualization

Composite DJI Trade Strategy vs DJI

BacktestReports.createTearsheetReport(\
                    backtest                   = tsExecuteEngine,
                    ta_reg_item_name           = "all_^DJI",
                    ta_reg_item_benchmark_name = "Long-Only Trade Strategy for ^DJI ", # optional
                    report_type                = "full" )

Performance Metrics

                           Strategy    Benchmark
-------------------------  ----------  -----------
Start Period               2011-01-04  2011-01-04
End Period                 2012-12-31  2012-12-31
Risk-Free Rate             0.0%        0.0%
Time in Market             100.0%      100.0%

Cumulative Return          18.33%      12.26%
CAGR%                      8.82%       5.98%

Sharpe                     0.58        0.43
Sortino                    0.86        0.59
Sortino/√2                 0.61        0.42

Max Drawdown               -20.09%     -16.79%
Longest DD Days            133         277
Volatility (ann.)          17.21%      17.02%
R^2                        0.0         0.0
Calmar                     0.44        0.36
Skew                       0.04        -0.41
Kurtosis                   3.55        3.7

Expected Daily %           0.03%       0.02%
Expected Monthly %         0.7%        0.48%
Expected Yearly %          8.78%       5.95%
Kelly Criterion            4.2%        9.47%
Risk of Ruin               0.0%        0.0%
Daily Value-at-Risk        -1.74%      -1.73%
Expected Shortfall (cVaR)  -1.74%      -1.73%

Gain/Pain Ratio            0.11        0.08
Gain/Pain (1M)             0.93        0.6

Payoff Ratio               1.12        1.12
Profit Factor              1.11        1.08
Common Sense Ratio         1.3         1.06
CPC Index                  0.62        0.63
Tail Ratio                 1.17        0.98
Outlier Win Ratio          3.59        3.9
Outlier Loss Ratio         4.38        4.13

MTD                        1.03%       0.6%
3M                         1.19%       -2.47%
6M                         1.13%       1.74%
YTD                        13.74%      7.24%
1Y                         13.74%      7.24%
3Y (ann.)                  8.82%       5.98%
5Y (ann.)                  8.82%       5.98%
10Y (ann.)                 8.82%       5.98%
All-time (ann.)            8.82%       5.98%

Best Day                   4.61%       4.23%
Worst Day                  -5.53%      -5.53%
Best Month                 13.15%      9.52%
Worst Month                -5.01%      -6.2%
Best Year                  13.74%      7.24%
Worst Year                 4.04%       4.68%

Avg. Drawdown              -3.45%      -2.31%
Avg. Drawdown Days         24          27
Recovery Factor            0.91        0.73
Ulcer Index                1.06        1.08

Avg. Up Month              2.19%       1.84%
Avg. Down Month            -2.05%      -2.73%
Win Days %                 49.3%       52.1%
Win Month %                58.33%      66.67%
Win Quarter %              62.5%       62.5%
Win Year %                 100.0%      100.0%
Beta                       0.06        -
Alpha                      0.1         -

None

5 Worst Drawdowns

	Start	Valley	End	Days	Max Drawdown	99% Max Drawdown
1	2011-05-11	2011-08-08	2011-09-21	133	-20.092738	-16.926920
2	2011-11-14	2011-12-07	2012-03-13	120	-10.702331	-10.322832
3	2012-04-19	2012-05-18	2012-06-29	71	-8.870995	-8.334880
4	2011-10-06	2011-11-01	2011-11-11	36	-7.587114	-7.585521
5	2012-11-19	2012-12-18	2012-12-31	42	-6.034787	-5.724283

Strategy Visualization

Portfolio

BacktestReports.createTearsheetReport(\
                    backtest                   = tsExecuteEngine,
                    ta_reg_item_name           = "Portfolio of US-Indexes with TechAnal & AI Trading Models, and Long strategies",
                    ta_reg_item_benchmark_name = None, # optional
                    report_type                = "full" )

Performance Metrics

                           Strategy
-------------------------  ----------
Start Period               2011-01-04
End Period                 2012-12-31
Risk-Free Rate             0.0%
Time in Market             100.0%

Cumulative Return          10.04%
CAGR%                      4.92%

Sharpe                     0.58
Sortino                    0.85
Sortino/√2                 0.6

Max Drawdown               -10.64%
Longest DD Days            252
Volatility (ann.)          9.0%
Calmar                     0.46
Skew                       -0.15
Kurtosis                   3.31

Expected Daily %           0.02%
Expected Monthly %         0.4%
Expected Yearly %          4.9%
Kelly Criterion            5.14%
Risk of Ruin               0.0%
Daily Value-at-Risk        -0.91%
Expected Shortfall (cVaR)  -0.91%

Gain/Pain Ratio            0.12
Gain/Pain (1M)             0.66

Payoff Ratio               1.18
Profit Factor              1.12
Common Sense Ratio         1.35
CPC Index                  0.64
Tail Ratio                 1.21
Outlier Win Ratio          3.89
Outlier Loss Ratio         4.85

MTD                        1.01%
3M                         -1.8%
6M                         0.05%
YTD                        7.71%
1Y                         7.71%
3Y (ann.)                  4.92%
5Y (ann.)                  4.92%
10Y (ann.)                 4.92%
All-time (ann.)            4.92%

Best Day                   2.1%
Worst Day                  -2.78%
Best Month                 5.32%
Worst Month                -3.52%
Best Year                  7.71%
Worst Year                 2.17%

Avg. Drawdown              -1.18%
Avg. Drawdown Days         21
Recovery Factor            0.94
Ulcer Index                1.06

Avg. Up Month              1.86%
Avg. Down Month            -1.59%
Win Days %                 48.7%
Win Month %                58.33%
Win Quarter %              50.0%
Win Year %                 100.0%

None

5 Worst Drawdowns

	Start	Valley	End	Days	Max Drawdown	99% Max Drawdown
1	2011-03-04	2011-08-08	2011-11-11	252	-10.642112	-9.051853
2	2012-04-26	2012-05-18	2012-09-06	133	-5.370260	-5.015202
3	2011-11-16	2011-11-25	2012-01-17	62	-4.817825	-4.580090
4	2012-11-08	2012-12-11	2012-12-31	53	-4.146542	-4.088048
5	2012-04-03	2012-04-10	2012-04-25	22	-3.034206	-2.361398

Strategy Visualization

HTML Tearsheet Reports

BacktestReports.createHTMLTearsheetReport(\
                    backtest                   = tsExecuteEngine,
                    ta_reg_item_name           = "all_^IXIC",
                    ta_reg_item_benchmark_name = "Long-Only Trade Strategy for ^IXIC ", # optional
                    title                      = 'Strategy Tearsheet', 
                    download_filename          = 'ts-tearsheet.html' )